diff --git a/app/withings/README.md b/app/withings/README.md
index 143c941..d3cdd43 100644
--- a/app/withings/README.md
+++ b/app/withings/README.md
@@ -10,7 +10,7 @@ We use the following devices for vitals data measurement:
* Heart Rate, SPO2
* [Withings Thermo](https://www.withings.com/de/en/thermo)
* Body Surface Temperature
-* [WIthings BPM Core](https://www.withings.com/de/en/bpm-core)
+* [Withings BPM Core](https://www.withings.com/de/en/bpm-core)
* Blood Pressure
## API Access
diff --git a/docs/bibliography/bibliography.bib b/docs/bibliography/bibliography.bib
index f74bbe1..f84ed02 100644
--- a/docs/bibliography/bibliography.bib
+++ b/docs/bibliography/bibliography.bib
@@ -1,4 +1,349 @@
+@online{zarabzadeh_features_2012,
+ title = {Features of electronic Early Warning systems which impact clinical decision making {\textbar} {IEEE} Conference Publication {\textbar} {IEEE} Xplore},
+ url = {https://ieeexplore.ieee.org/document/6266394},
+ author = {Zarabzadeh, Atieh},
+ urldate = {2023-04-26},
+ date = {2012},
+ file = {Features of electronic Early Warning systems which impact clinical decision making | IEEE Conference Publication | IEEE Xplore:/home/ulinja/Zotero/storage/Q9BI6RWR/6266394.html:text/html;Features of electronic Early Warning systems which.pdf:/home/ulinja/Zotero/storage/SSHGFSTF/Features of electronic Early Warning systems which.pdf:application/pdf},
+}
+
+@article{otoom_iot-based_2020,
+ title = {An {IoT}-based framework for early identification and monitoring of {COVID}-19 cases},
+ volume = {62},
+ issn = {1746-8094},
+ url = {https://www.sciencedirect.com/science/article/pii/S1746809420302949},
+ doi = {10.1016/j.bspc.2020.102149},
+ abstract = {The world has been facing the challenge of {COVID}-19 since the end of 2019. It is expected that the world will need to battle the {COVID}-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time {COVID}-19 detection and monitoring system. The proposed system would employ an Internet of Things ({IoTs}) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine ({SVM}), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-{NN}), Decision Table, Decision Stump, {OneR}, and {ZeroR}. An experiment was conducted to test these eight algorithms on a real {COVID}-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 \%. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of {COVID}-19, and the framework would then document the treatment response for each patient who has contracted the virus.},
+ pages = {102149},
+ journaltitle = {Biomedical Signal Processing and Control},
+ shortjournal = {Biomedical Signal Processing and Control},
+ author = {Otoom, Mwaffaq and Otoum, Nesreen and Alzubaidi, Mohammad A. and Etoom, Yousef and Banihani, Rudaina},
+ urldate = {2023-04-27},
+ date = {2020-09-01},
+ langid = {english},
+ keywords = {Coronaviruses, {COVID}-19, Early identification or prediction, Internet of Things, Real-time monitoring, Treatment response},
+ file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/NCS9RXIF/Otoom et al. - 2020 - An IoT-based framework for early identification an.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/ZS8ARH8Q/S1746809420302949.html:text/html},
+}
+
+@online{noauthor_national_2017,
+ title = {National Early Warning Score ({NEWS}) 2},
+ url = {https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2},
+ abstract = {{NEWS}2 is the latest version of the National Early Warning Score ({NEWS}), first produced in 2012 and updated in December 2017, which advocates a system to standardise the assessment and response to acute illness.},
+ titleaddon = {{RCP} London},
+ urldate = {2023-05-01},
+ date = {2017-12-19},
+ file = {Snapshot:/home/ulinja/Zotero/storage/TMN5DTXM/national-early-warning-score-news-2.html:text/html},
+}
+
+@article{smith_ability_2013,
+ title = {The ability of the National Early Warning Score ({NEWS}) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death},
+ volume = {84},
+ issn = {1873-1570},
+ doi = {10.1016/j.resuscitation.2012.12.016},
+ abstract = {{INTRODUCTION}: Early warning scores ({EWS}) are recommended as part of the early recognition and response to patient deterioration. The Royal College of Physicians recommends the use of a National Early Warning Score ({NEWS}) for the routine clinical assessment of all adult patients.
+{METHODS}: We tested the ability of {NEWS} to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit ({ICU}) admission or death within 24h of a {NEWS} value and compared its performance to that of 33 other {EWSs} currently in use, using the area under the receiver-operating characteristic ({AUROC}) curve and a large vital signs database (n=198,755 observation sets) collected from 35,585 consecutive, completed acute medical admissions.
+{RESULTS}: The {AUROCs} (95\% {CI}) for {NEWS} for cardiac arrest, unanticipated {ICU} admission, death, and any of the outcomes, all within 24h, were 0.722 (0.685-0.759), 0.857 (0.847-0.868), 0.894 (0.887-0.902), and 0.873 (0.866-0.879), respectively. Similarly, the ranges of {AUROCs} (95\% {CI}) for the other 33 {EWSs} were 0.611 (0.568-0.654) to 0.710 (0.675-0.745) (cardiac arrest); 0.570 (0.553-0.568) to 0.827 (0.814-0.840) (unanticipated {ICU} admission); 0.813 (0.802-0.824) to 0.858 (0.849-0.867) (death); and 0.736 (0.727-0.745) to 0.834 (0.826-0.842) (any outcome).
+{CONCLUSIONS}: {NEWS} has a greater ability to discriminate patients at risk of the combined outcome of cardiac arrest, unanticipated {ICU} admission or death within 24h of a {NEWS} value than 33 other {EWSs}.},
+ pages = {465--470},
+ number = {4},
+ journaltitle = {Resuscitation},
+ shortjournal = {Resuscitation},
+ author = {Smith, Gary B. and Prytherch, David R. and Meredith, Paul and Schmidt, Paul E. and Featherstone, Peter I.},
+ date = {2013-04},
+ pmid = {23295778},
+ keywords = {Aged, Early Diagnosis, Female, Heart Arrest, Hospital Mortality, Humans, Intensive Care Units, Male, Patient Admission, Risk Assessment, {ROC} Curve, Severity of Illness Index, United Kingdom, Vital Signs},
+ file = {Accepted Version:/home/ulinja/Zotero/storage/WKEEUEAW/Smith et al. - 2013 - The ability of the National Early Warning Score (N.pdf:application/pdf},
+}
+
+@inproceedings{kim_two_2007,
+ location = {Berlin, Heidelberg},
+ title = {Two Algorithms for Detecting Respiratory Rate from {ECG} Signal},
+ isbn = {978-3-540-36841-0},
+ doi = {10.1007/978-3-540-36841-0_1030},
+ series = {{IFMBE} Proceedings},
+ abstract = {Wearable real-time health monitoring technology has been developed for remote diagnosis and health check during daily life. The present study proposes two algorithms to detect respiratory rate from {ECG} signal. One gets respiratory rate by measuring the number of {ECG} samples in R-R interval and its advantage lies in its simplicity. The other detects the rate by measuring the size of R wave in {QRS} signal. This algorithm can detect the rate more robustly but it is complicated and requires the {ECG} signal base line to be stabilized. The preliminary study in laboratory environment showed that the precision of these algorithms was over 97\%.},
+ pages = {4069--4071},
+ booktitle = {World Congress on Medical Physics and Biomedical Engineering 2006},
+ publisher = {Springer},
+ author = {Kim, J. M. and Hong, J. H. and Kim, N. J. and Cha, E. J. and Lee, Tae-Soo},
+ editor = {Magjarevic, R. and Nagel, J. H.},
+ date = {2007},
+ langid = {english},
+ keywords = {{ECG}, {EDR}, {QRS}, R-R interval},
+ file = {Kim et al. - 2007 - Two Algorithms for Detecting Respiratory Rate from.pdf:/home/ulinja/Zotero/storage/YNEGUM7M/Kim et al. - 2007 - Two Algorithms for Detecting Respiratory Rate from.pdf:application/pdf},
+}
+
+@article{subbe_validation_2001,
+ title = {Validation of a modified Early Warning Score in medical admissions},
+ volume = {94},
+ issn = {1460-2725},
+ url = {https://doi.org/10.1093/qjmed/94.10.521},
+ doi = {10.1093/qjmed/94.10.521},
+ abstract = {The Early Warning Score ({EWS}) is a simple physiological scoring system suitable for bedside application. The ability of a modified Early Warning Score ({MEWS}) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated. In a prospective cohort study, we applied {MEWS} to patients admitted to the 56‐bed acute Medical Admissions Unit ({MAU}) of a District General Hospital ({DGH}). Data on 709 medical emergency admissions were collected during March 2000. Main outcome measures were death, intensive care unit ({ICU}) admission, high dependency unit ({HDU}) admission, cardiac arrest, survival and hospital discharge at 60 days. Scores of 5 or more were associated with increased risk of death ({OR} 5.4, 95\%{CI} 2.8–10.7), {ICU} admission ({OR} 10.9, 95\%{CI} 2.2–55.6) and {HDU} admission ({OR} 3.3, 95\%{CI} 1.2–9.2). {MEWS} can be applied easily in a {DGH} medical admission unit, and identifies patients at risk of deterioration who require increased levels of care in the {HDU} or {ICU}. A clinical pathway could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management.},
+ pages = {521--526},
+ number = {10},
+ journaltitle = {{QJM}: An International Journal of Medicine},
+ shortjournal = {{QJM}: An International Journal of Medicine},
+ author = {Subbe, C.P. and Kruger, M. and Rutherford, P. and Gemmel, L.},
+ urldate = {2023-04-30},
+ date = {2001-10-01},
+ file = {Full Text PDF:/home/ulinja/Zotero/storage/P7TJ5DJB/Subbe et al. - 2001 - Validation of a modified Early Warning Score in me.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/FFJJTX3I/1558977.html:text/html},
+}
+
+@article{abbott_pre-hospital_2018,
+ title = {Pre-hospital National Early Warning Score ({NEWS}) is associated with in-hospital mortality and critical care unit admission: A cohort study},
+ volume = {27},
+ issn = {2049-0801},
+ url = {https://www.sciencedirect.com/science/article/pii/S2049080118300116},
+ doi = {10.1016/j.amsu.2018.01.006},
+ shorttitle = {Pre-hospital National Early Warning Score ({NEWS}) is associated with in-hospital mortality and critical care unit admission},
+ abstract = {Background
+National Early Warning Score ({NEWS}) is increasingly used in {UK} hospitals. However, there is only limited evidence to support the use of pre-hospital early warning scores. We hypothesised that pre-hospital {NEWS} was associated with death or critical care escalation within the first 48 h of hospital stay.
+Methods
+Planned secondary analysis of a prospective cohort study at a single {UK} teaching hospital. Consecutive medical ward admissions over a 20-day period were included in the study. Data were collected from ambulance report forms, medical notes and electronic patient records. Pre-hospital {NEWS} was calculated retrospectively. The primary outcome was a composite of death or critical care unit escalation within 48 h of hospital admission. The secondary outcome was length of hospital stay.
+Results
+189 patients were included in the analysis. The median pre-hospital {NEWS} was 3 ({IQR} 1–5). 13 patients (6.9\%) died or were escalated to the critical care unit within 48 h of hospital admission. Pre-hospital {NEWS} was associated with death or critical care unit escalation ({OR}, 1.25; 95\% {CI}, 1.04–1.51; p = 0.02), but {NEWS} on admission to hospital was more strongly associated with this outcome ({OR}, 1.52; 95\% {CI}, 1.18–1.97, p {\textless} 0.01). Neither was associated with hospital length of stay.
+Conclusion
+Pre-hospital {NEWS} was associated with death or critical care unit escalation within 48 h of hospital admission. {NEWS} could be used by ambulance crews to assist in the early triage of patients requiring hospital treatment or rapid transport. Further cohort studies or trials in large samples are required before implementation.},
+ pages = {17--21},
+ journaltitle = {Annals of Medicine and Surgery},
+ shortjournal = {Annals of Medicine and Surgery},
+ author = {Abbott, Tom E. F. and Cron, Nicholas and Vaid, Nidhi and Ip, Dorothy and Torrance, Hew D. T. and Emmanuel, Julian},
+ urldate = {2023-04-28},
+ date = {2018-03-01},
+ langid = {english},
+ keywords = {Acute care emergency ambulance systems, Clinical research, Intensive care, Pre-hospital},
+ file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/2HPZCFXG/Abbott et al. - 2018 - Pre-hospital National Early Warning Score (NEWS) i.pdf:application/pdf},
+}
+
+@article{martin-rodriguez_analysis_2019,
+ title = {Analysis of the early warning score to detect critical or high-risk patients in the prehospital setting},
+ volume = {14},
+ issn = {1970-9366},
+ url = {https://doi.org/10.1007/s11739-019-02026-2},
+ doi = {10.1007/s11739-019-02026-2},
+ abstract = {The early warning score can help to prevent, recognize and act at the first signs of clinical and physiological deterioration. The objective of this study is to evaluate different scales for use in the prehospital setting and to select the most relevant one by applicability and capacity to predict mortality in the first 48 h. A prospective longitudinal observational study was conducted in patients over 18 years of age who were treated by the advanced life support unit and transferred to the emergency department between April and July 2018. We analyzed demographic variables as well as the physiological parameters and clinical observations necessary to complement the {EWS}. Subsequently, each patient was followed up, considering their final diagnosis and mortality data. A total of 349 patients were included in our study. Early mortality before the first 48 h affected 27 patients (7.7\%). The scale with the best capacity to predict early mortality was the National Early Warning Score 2, with an area under the curve of 0.896 (95\% {CI} 0.82–0.97). The score with the lowest global classification error was 10 points with sensitivity of 81.5\% (95\% {CI} 62.7–92.1) and specificity of 88.5\% (95\% {CI} 84.5–91.6). The early warning score studied (except modified early warning score) shows no statistically significant differences between them; however, the National Early Warning Score 2 is the most used score internationally, validated at the prehospital scope and with a wide scientific literature that supports its use. The Prehospital Emergency Medical Services should include this scale among their operative elements to complement the structured and objective evaluation of the critical patient.},
+ pages = {581--589},
+ number = {4},
+ journaltitle = {Internal and Emergency Medicine},
+ shortjournal = {Intern Emerg Med},
+ author = {Martín-Rodríguez, Francisco and Castro-Villamor, Miguel Ángel and del Pozo Vegas, Carlos and Martín-Conty, José Luis and Mayo-Iscar, Agustín and Delgado Benito, Juan Francisco and del Brio Ibañez, Pablo and Arnillas-Gómez, Pedro and Escudero-Cuadrillero, Carlos and López-Izquierdo, Raúl},
+ urldate = {2023-04-28},
+ date = {2019-06-01},
+ langid = {english},
+ keywords = {Clinical research, Early mortality, Early warning score, Prehospital care, Prognosis},
+ file = {Full Text PDF:/home/ulinja/Zotero/storage/2LVIYDZR/Martín-Rodríguez et al. - 2019 - Analysis of the early warning score to detect crit.pdf:application/pdf},
+}
+
+@article{wu_predicting_2021,
+ title = {Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score ({MEWS}) and machine learning approach},
+ volume = {9},
+ issn = {2167-8359},
+ url = {https://peerj.com/articles/11988},
+ doi = {10.7717/peerj.11988},
+ shorttitle = {Predicting in-hospital mortality in adult non-traumatic emergency department patients},
+ abstract = {Background A feasible and accurate risk prediction systems for emergency department ({ED}) patients is urgently required. The Modified Early Warning Score ({MEWS}) is a wide-used tool to predict clinical outcomes in {ED}. Literatures showed that machine learning ({ML}) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a {ML} model to predict in-hospital morality of the adult non traumatic {ED} patients for different time stages, and comparing performance with other {ML} models and {MEWS}. Methods A retrospective observational cohort study was conducted in five Taiwan {EDs} including two tertiary medical centers and three regional hospitals. All consecutively adult ({\textgreater}17 years old) non-traumatic patients admit to {ED} during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. {MEWS} was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking {ML} model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic ({AUROC}) and the area under the precision and recall curve ({AUPRC}) as the comparative measures. Result After excluding 182,001 visits (7.46\%), study group was consisted of 24,37,326 {ED} visits. The dataset was split into 67\% training data and 33\% test data for {ML} model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the {AUROC} of {MEW} and {ML} mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking {ML} model outperform other {ML} model as well. For the prediction of in-hospital mortality over 48-hours, {AUPRC} performance of {MEWS} drop below 0.1, while the {AUPRC} of {ML} mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, {ML} model achieved statistically significant higher {AUROC} and {AUPRC} than {MEWS} (all P {\textless} 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of {AUROC} values between two model increases gradually (P {\textless} 0.001). Three {MEWS} thresholds (score {\textgreater}3, {\textgreater}4, and {\textgreater}5) were determined as baselines for comparison, {ML} mode consistently showed improved or equally performance in sensitivity, {PPV}, {NPV}, but not in specific. Conclusion Stacking {ML} methods improve predicted in-hospital mortality than {MEWS} in adult non-traumatic {ED} patients, especially in the prediction of delayed mortality.},
+ pages = {e11988},
+ journaltitle = {{PeerJ}},
+ shortjournal = {{PeerJ}},
+ author = {Wu, Kuan-Han and Cheng, Fu-Jen and Tai, Hsiang-Ling and Wang, Jui-Cheng and Huang, Yii-Ting and Su, Chih-Min and Chang, Yun-Nan},
+ urldate = {2023-04-28},
+ date = {2021-08-24},
+ langid = {english},
+ note = {Publisher: {PeerJ} Inc.},
+ file = {Full Text PDF:/home/ulinja/Zotero/storage/H2MPDP9A/Wu et al. - 2021 - Predicting in-hospital mortality in adult non-trau.pdf:application/pdf},
+}
+
+@online{noauthor_medtronic_nodate,
+ title = {Medtronic {BioButton} {\textbar} Multi-parameter Wearable},
+ url = {https://www.medtronic.com/covidien/en-us/products/remote-monitoring/healthcast-intelligent-patient-manager/healthcast-biobutton-multi-parameter-wearable.html},
+ urldate = {2023-04-27},
+ file = {BioButton®* Multi-parameter Wearable | Medtronic:/home/ulinja/Zotero/storage/Z5TF3VAL/healthcast-biobutton-multi-parameter-wearable.html:text/html},
+}
+
+@online{noauthor_caretaker_nodate,
+ title = {Caretaker Medical {VitalStream}},
+ url = {https://caretakermedical.net/},
+ abstract = {{VitalStream} is the new standard in wireless patient monitoring. The device is clinically validated and {FDA} cleared.},
+ urldate = {2023-04-27},
+ langid = {english},
+ file = {Snapshot:/home/ulinja/Zotero/storage/UGJRJ7A4/caretakermedical.net.html:text/html},
+}
+
+@online{noauthor_vitls_nodate,
+ title = {Vitls Tego - Vitals monitoring device for infants},
+ url = {https://www.vitlsinc.com/unique-features},
+ abstract = {Our wearable medical device has tackled the downsides to current vital monitoring options and engineered the ultimate way to care for your patients without the hassle.},
+ titleaddon = {Vitls},
+ urldate = {2023-04-27},
+ langid = {american},
+ file = {Snapshot:/home/ulinja/Zotero/storage/K8NGCBH5/unique-features.html:text/html},
+}
+
+@online{noauthor_equivital_nodate,
+ title = {Equivital {LifeMonitor} - Mobile vital signs monitor},
+ url = {https://equivital.com/mobile-vital-signs-monitor},
+ abstract = {Equivital’s {LifeMonitor} is a body worn sensor which measures {ECG}, heart rate, breathing rate, skin temperature, activity and body position.},
+ titleaddon = {Equivital},
+ urldate = {2023-04-27},
+ langid = {british},
+ file = {Snapshot:/home/ulinja/Zotero/storage/GBSYE3DG/mobile-vital-signs-monitor.html:text/html},
+}
+
+@online{noauthor_visi_nodate,
+ title = {Visi Mobile - Patient Vital Signs Monitoring System {\textbar} Sotera Digital Health},
+ url = {https://soteradigitalhealth.com},
+ abstract = {Sotera Digital Health make continuous patient monitoring system as the new standard of care for step-down and/or general floor units.},
+ urldate = {2023-04-27},
+ langid = {english},
+ file = {Snapshot:/home/ulinja/Zotero/storage/SKUUNC7F/soteradigitalhealth.com.html:text/html},
+}
+
+@article{carr_evaluation_2021,
+ title = {Evaluation and improvement of the National Early Warning Score ({NEWS}2) for {COVID}-19: a multi-hospital study},
+ volume = {19},
+ issn = {1741-7015},
+ url = {https://doi.org/10.1186/s12916-020-01893-3},
+ doi = {10.1186/s12916-020-01893-3},
+ shorttitle = {Evaluation and improvement of the National Early Warning Score ({NEWS}2) for {COVID}-19},
+ abstract = {The National Early Warning Score ({NEWS}2) is currently recommended in the {UK} for the risk stratification of {COVID}-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate {NEWS}2 for the prediction of severe {COVID}-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of {NEWS}2 alone for medium-term risk stratification.},
+ pages = {23},
+ number = {1},
+ journaltitle = {{BMC} Medicine},
+ shortjournal = {{BMC} Med},
+ author = {Carr, Ewan and Bendayan, Rebecca and Bean, Daniel and Stammers, Matt and Wang, Wenjuan and Zhang, Huayu and Searle, Thomas and Kraljevic, Zeljko and Shek, Anthony and Phan, Hang T. T. and Muruet, Walter and Gupta, Rishi K. and Shinton, Anthony J. and Wyatt, Mike and Shi, Ting and Zhang, Xin and Pickles, Andrew and Stahl, Daniel and Zakeri, Rosita and Noursadeghi, Mahdad and O’Gallagher, Kevin and Rogers, Matt and Folarin, Amos and Karwath, Andreas and Wickstrøm, Kristin E. and Köhn-Luque, Alvaro and Slater, Luke and Cardoso, Victor Roth and Bourdeaux, Christopher and Holten, Aleksander Rygh and Ball, Simon and {McWilliams}, Chris and Roguski, Lukasz and Borca, Florina and Batchelor, James and Amundsen, Erik Koldberg and Wu, Xiaodong and Gkoutos, Georgios V. and Sun, Jiaxing and Pinto, Ashwin and Guthrie, Bruce and Breen, Cormac and Douiri, Abdel and Wu, Honghan and Curcin, Vasa and Teo, James T. and Shah, Ajay M. and Dobson, Richard J. B.},
+ urldate = {2023-04-27},
+ date = {2021-01-21},
+ langid = {english},
+ keywords = {{COVID}-19, Blood parameters, {NEWS}2 score, Prediction model},
+ file = {Full Text PDF:/home/ulinja/Zotero/storage/4RTVXPRT/Carr et al. - 2021 - Evaluation and improvement of the National Early W.pdf:application/pdf},
+}
+
+@article{filho_iot-based_2021,
+ title = {An {IoT}-Based Healthcare Platform for Patients in {ICU} Beds During the {COVID}-19 Outbreak},
+ volume = {9},
+ issn = {2169-3536},
+ doi = {10.1109/ACCESS.2021.3058448},
+ abstract = {There is a global concern with the escalating number of patients at hospitals caused mainly by population aging, chronic diseases, and recently by the {COVID}-19 outbreak. To smooth this challenge, {IoT} emerges as an encouraging paradigm because it provides the scalability required for this purpose, supporting continuous and reliable health monitoring on a global scale. Based on this context, an {IoT}-based healthcare platform to provide remote monitoring for patients in a critical situation was proposed in the authors’ previous works. Therefore, this paper aims to extend the platform by integrating wearable and unobtrusive sensors to monitor patients with coronavirus disease. Furthermore, we report a real deployment of our approach in an intensive care unit for {COVID}-19 patients in Brazil.},
+ pages = {27262--27277},
+ journaltitle = {{IEEE} Access},
+ author = {Filho, Itamir de Morais Barroca and Aquino, Gibeon and Malaquias, Ramon Santos and Girão, Gustavo and Melo, Sávio Rennan Menêzes},
+ date = {2021},
+ note = {Conference Name: {IEEE} Access},
+ keywords = {{COVID}-19, Internet of Things, Biomedical monitoring, Cloud computing, Healthcare, Medical services, Monitoring, platform, Protocols, remote monitoring, Sensors},
+ file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/QJRQD4DV/9351912.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/Z47T3IBP/Filho et al. - 2021 - An IoT-Based Healthcare Platform for Patients in I.pdf:application/pdf},
+}
+
+@article{gidari_predictive_2020,
+ title = {Predictive value of National Early Warning Score 2 ({NEWS}2) for intensive care unit admission in patients with {SARS}-{CoV}-2 infection},
+ volume = {52},
+ issn = {2374-4235},
+ url = {https://doi.org/10.1080/23744235.2020.1784457},
+ doi = {10.1080/23744235.2020.1784457},
+ abstract = {Background: From January 2020, Coronavirus disease 19 ({COVID}-19) has rapidly spread all over the world. An early assessment of illness severity is important for the stratification of patients. We analysed the predictive value of National Early Warning Score 2 ({NEWS}2) for intensive care unit admission ({ICU}) in patients with Severe Acute Respiratory Syndrome- Coronavirus-2 ({SARS}-{CoV}-2) infection.Methods: Data of 71 patients with {SARS}-{CoV}-2 admitted from 1 March to 20 April 2020, to the Clinic of Infectious Diseases of Perugia Hospital, Italy, were retrospectively reviewed. {NEWS}2 at hospital admission, demographic, comorbidity and clinical data were collected. Univariate and multivariate analyses were performed to establish the correlation between each variable and {ICU} admission.Results: Among 68 patients included in the analysis, 27 were admitted to {ICU}. {NEWS}2 at hospital admission was a good predictor of {ICU} admission as shown by an area under the receiver-operating characteristic curve analysis of 0.90 (standard error 0.04; 95\% confidence interval 0.82–0.97). In multivariate logistic regression analysis, {NEWS}2 was significantly related to {ICU} admission using thresholds of 5 and 7. No other clinical variables included in the model were significantly correlated with {ICU} admission.A {NEWS}2 threshold of 5 had higher sensitivity than a threshold of 7 (89\% and 63\%). Higher specificity, positive likelihood ratio and positive predictive value were found using a threshold of 7 than a threshold of 5.Conclusions: {NEWS}2 at hospital admission was a good predictor for {ICU} admission. Patients with severe {COVID}-19 were correctly and rapidly stratified.},
+ pages = {698--704},
+ number = {10},
+ journaltitle = {Infectious Diseases},
+ author = {Gidari, Anna and De Socio, Giuseppe Vittorio and Sabbatini, Samuele and Francisci, Daniela},
+ urldate = {2023-04-27},
+ date = {2020-10-02},
+ pmid = {32584161},
+ note = {Publisher: Taylor \& Francis
+\_eprint: https://doi.org/10.1080/23744235.2020.1784457},
+ keywords = {{COVID}-19, {ICU}, National Early Warning Score 2, {NEWS}2, {SARS}-{CoV}-2},
+}
+
+@article{bilben_national_2016,
+ title = {National Early Warning Score ({NEWS}) as an emergency department predictor of disease severity and 90-day survival in the acutely dyspneic patient – a prospective observational study},
+ volume = {24},
+ rights = {2016 The Author(s).},
+ issn = {1757-7241},
+ url = {https://sjtrem.biomedcentral.com/articles/10.1186/s13049-016-0273-9},
+ doi = {10.1186/s13049-016-0273-9},
+ abstract = {National Early Warning Score ({NEWS}) was designed to detect deteriorating patients in hospital wards, specifically those at increased risk of {ICU} admission, cardiac arrest, or death within 24 h. {NEWS} is not validated for use in Emergency Departments ({ED}), but emerging data suggest it may be useful. A criticism of {NEWS} is that patients with chronic poor oxygenation, e.g. severe chronic obstructive pulmonary disease ({COPD}), will have elevated {NEWS} also in the absence of acute deterioration, possibly reducing the predictive power of {NEWS} in this subgroup. We wanted to prospectively evaluate the usefulness of {NEWS} in unselected adult patients emergently presenting in a Norwegian {ED} with respiratory distress as main symptom. In respiratory distressed patients, {NEWS} was calculated on {ED} arrival, after 2–4 h, and the next day. Manchester Triage Scale ({MTS}) category, age, gender, comorbidity ({ASA} score), {ICU}-admission, ventilatory support, and discharge diagnoses were noted. Survival status was tracked for {\textgreater}90 days through the Population Registry. Data are medians (25–75th percentiles). Factors predicting 90-day survival were analysed with multiple logistic regression. We included 246 patients; 71 years old (60–80), 89 \% home-dwelling, 74 \% {ASA} 3–4, 72 \% {MTS} 1–2, 88 \% admitted to hospital. {NEWS} on arrival was 5 (3–7). {NEWS} correlated closely with {MTS} category and maximum in-hospital level of care ({ED}, ward, high-dependency unit, {ICU}). Sixteen patients died in-hospital, 26 died after discharge within 90 days. Controlled for age, {ASA} score, and {COPD}, a higher {NEWS} on {ED} arrival predicted poorer 90-day survival. Increased {NEWS} also correlated with decreased 30-day- and in-hospital survival and a decreased probability for home-dwelling patients to be discharged directly home. In respiratory distressed patients, {NEWS} on {ED} arrival correlated closely with triage category and need of {ICU} admission and predicted long-term out-of-hospital survival controlled for age, comorbidity, and {COPD}. {NEWS} should be explored in the {ED} setting to determine its role in clinical decision-making and in communication along the acute care chain.},
+ pages = {1--8},
+ number = {1},
+ journaltitle = {Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine},
+ shortjournal = {Scand J Trauma Resusc Emerg Med},
+ author = {Bilben, Bente and Grandal, Linda and Søvik, Signe},
+ urldate = {2023-04-27},
+ date = {2016-12},
+ langid = {english},
+ note = {Number: 1
+Publisher: {BioMed} Central},
+ file = {Full Text PDF:/home/ulinja/Zotero/storage/YAGFBLNR/Bilben et al. - 2016 - National Early Warning Score (NEWS) as an emergenc.pdf:application/pdf},
+}
+
+@article{alam_exploring_2015,
+ title = {Exploring the performance of the National Early Warning Score ({NEWS}) in a European emergency department},
+ volume = {90},
+ issn = {0300-9572},
+ url = {https://www.sciencedirect.com/science/article/pii/S0300957215000787},
+ doi = {10.1016/j.resuscitation.2015.02.011},
+ abstract = {Background
+Several triage systems have been developed for use in the emergency department ({ED}), however they are not designed to detect deterioration in patients. Deteriorating patients may be at risk of going undetected during their {ED} stay and are therefore vulnerable to develop serious adverse events ({SAEs}). The National Early Warning Score ({NEWS}) has a good ability to discriminate ward patients at risk of {SAEs}. The utility of {NEWS} had not yet been studied in an {ED}.
+Objective
+To explore the performance of the {NEWS} in an {ED} with regard to predicting adverse outcomes.
+Design
+A prospective observational study. Patients Eligible patients were those presenting to the {ED} during the 6 week study period with an Emergency Severity Index ({ESI}) of 2 and 3 not triaged to the resuscitation room.
+Intervention
+{NEWS} was documented at three time points: on arrival (T0), hour after arrival (T1) and at transfer to the general ward/{ICU} (T2). The outcomes of interest were: hospital admission, {ICU} admission, length of stay and 30 day mortality.
+Results
+A total of 300 patients were assessed for eligibility. Complete data was able to be collected for 274 patients on arrival at the {ED}. {NEWS} was significantly correlated with patient outcomes, including 30 day mortality, hospital admission, and length of stay at all-time points.
+Conclusion
+The {NEWS} measured at different time points was a good predictor of patient outcomes and can be of additional value in the {ED} to longitudinally monitor patients throughout their stay in the {ED} and in the hospital.},
+ pages = {111--115},
+ journaltitle = {Resuscitation},
+ shortjournal = {Resuscitation},
+ author = {Alam, N. and Vegting, I. L. and Houben, E. and van Berkel, B. and Vaughan, L. and Kramer, M. H. H. and Nanayakkara, P. W. B.},
+ urldate = {2023-04-27},
+ date = {2015-05-01},
+ langid = {english},
+ keywords = {Early warning score, Monitoring, Clinical outcomes, Deteriorating patients, {NEWS}, Physiological parameters},
+ file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/HI4XZEPG/S0300957215000787.html:text/html},
+}
+
+@article{burgos-esteban_effectiveness_2022,
+ title = {Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review},
+ volume = {10},
+ rights = {cc by},
+ issn = {2296-2565},
+ url = {https://europepmc.org/articles/PMC9330632},
+ doi = {10.3389/fpubh.2022.894906},
+ shorttitle = {Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care},
+ abstract = {Background and {objectivesPatient} assessment and possible deterioration prediction are a healthcare priority. Increasing demand for outpatient emergency care services requires the implementation of simple, quick, and effective systems of patient evaluation and stratification. The purpose of this review is to identify the most effective Early Warning Score ({EWS}) for the early detection of the risk of complications when screening emergency outpatients for a potentially serious condition.Materials and {methodsSystematic} review of the bibliography made in 2022. Scientific articles in Spanish and English were collected from the databases and search engines of Pubmed, Cochrane, and Dialnet, which were published between 2017 and 2021 about {EWSs} and their capacity to predict complications.{ResultsFor} analysis eleven articles were selected. Eight dealt with the application of different early warning scores in outpatient situations, concluding that all the scoring systems they studied were applicable. Three evaluated the predictive ability of various scoring systems and found no significant differences in their results. The eight articles evaluated the suitability of {NEWS}/{NEWS}2 to outpatient conditions and concluded it was the most suitable in pre-hospital emergency settings.{ConclusionsThe} early warning scores that were studied can be applied at the pre-hospital level, as they can predict patient mortality in the short term (24 or 48 h) and support clinical patient evaluation and medical decision making. Among them, {NEWS}2 is the most suitable for screening potentially deteriorating medical emergency outpatients.},
+ pages = {894906},
+ journaltitle = {Frontiers in public health},
+ shortjournal = {Front Public Health},
+ author = {Burgos-Esteban, Amaya and Gea-Caballero, Vicente and Marín-Maicas, Patricia and Santillán-García, Azucena and Cordón-Hurtado, María de Valvanera and Marqués-Sule, Elena and Giménez-Luzuriaga, Marta and Juárez-Vela, Raúl and Sanchez-Gonzalez, Juan Luis and García-Criado, Jorge and Santolalla-Arnedo, Iván},
+ urldate = {2023-04-27},
+ date = {2022-01-01},
+ pmid = {35910902},
+ pmcid = {PMC9330632},
+ keywords = {Emergency Care, Emergency Medical Service (Ems), Emergency Medicine, Medicine, Scale},
+ file = {Full Text PDF (Open access):/home/ulinja/Zotero/storage/NFFHLGDV/Burgos-Esteban et al. - 2022 - Effectiveness of Early Warning Scores for Early Se.pdf:application/pdf},
+}
+
+@article{da_silva_deepsigns_2021,
+ title = {{DeepSigns}: A predictive model based on Deep Learning for the early detection of patient health deterioration},
+ volume = {165},
+ issn = {0957-4174},
+ url = {https://www.sciencedirect.com/science/article/pii/S0957417420307004},
+ doi = {10.1016/j.eswa.2020.113905},
+ shorttitle = {{DeepSigns}},
+ abstract = {Early diagnosis of critically ill patients depends on the attention and observation of medical staff about different variables, as vital signs, results of laboratory tests, among other. Seriously ill patients usually have changes in their vital signs before worsening. Monitoring these changes is important to anticipate the diagnosis in order to initiate patients’ care. Prognostic indexes play a fundamental role in this context since they allow to estimate the patients’ health status. Besides, the adoption of electronic health records improved the availability of data, which can be processed by machine learning techniques for information extraction to support clinical decisions. In this context, this work aims to create a computational model able to predict the deterioration of patients’ health status in such a way that it is possible to start the appropriate treatment as soon as possible. The model was developed based on Deep Learning technique, a Recurrent Neural Networks, the Long Short-Term Memory, for the prediction of patient’s vital signs and subsequent evaluation of the patient’s health status severity through Prognostic Indexes commonly used in the health area. Experiments showed that it is possible to predict vital signs with good precision (accuracy {\textgreater} 80\%) and, consequently, predict the Prognostic Indexes in advance to treat the patients before deterioration. Predicting the patient’s vital signs for the future and use them for the Prognostic Index’ calculation allows clinical times to predict future severe diagnoses that would not be possible applying the current patient’s vital signs (50\%–60\% of cases would not be identified).},
+ pages = {113905},
+ journaltitle = {Expert Systems with Applications},
+ shortjournal = {Expert Systems with Applications},
+ author = {da Silva, Denise Bandeira and Schmidt, Diogo and da Costa, Cristiano André and da Rosa Righi, Rodrigo and Eskofier, Björn},
+ urldate = {2023-04-27},
+ date = {2021-03-01},
+ langid = {english},
+ keywords = {Deep learning, Health informatics, {LSTM}, Machine learning, Predictive scores},
+ file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/FDRS6GKT/S0957417420307004.html:text/html},
+}
+
@article{pahlevanynejad_personalized_2023,
title = {Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges},
volume = {2023},
@@ -23,7 +368,7 @@
journaltitle = {Journal of King Saud University - Computer and Information Sciences},
author = {Imtyaz Ahmed, M. and Kannan, G.},
date = {2022},
- keywords = {Biosensors, Healthcare, Internet of Things, Privacy, Remote patient monitoring, Security},
+ keywords = {Internet of Things, Healthcare, Biosensors, Privacy, Remote patient monitoring, Security},
file = {Full Text:/home/ulinja/Zotero/storage/6ZW7G4RL/Imtyaz Ahmed and Kannan - 2022 - Secure and lightweight privacy preserving Internet.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/EFJRSHJQ/display.html:text/html},
}
@@ -35,7 +380,7 @@
eventtitle = {{MOBIHEALTH} 2015 - 5th {EAI} International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies},
author = {Anzanpour, A. and Rahmani, A.-M. and Liljeberg, P. and Tenhunen, H.},
date = {2015},
- keywords = {Body Area Network, {EarlyWarning} Score, Internet of Things, Remote Patient Monitoring, Wearable electronics, Wireless Sensor Network},
+ keywords = {Internet of Things, Body Area Network, {EarlyWarning} Score, Remote Patient Monitoring, Wearable electronics, Wireless Sensor Network},
file = {Full Text:/home/ulinja/Zotero/storage/37NIRLAE/Anzanpour et al. - 2015 - Internet of things enabled in-home health monitori.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/BSQHA7RC/display.html:text/html},
}
@@ -50,7 +395,7 @@
journaltitle = {Clinical and Experimental Emergency Medicine},
author = {Dagan, A. and Mechanic, O.J.},
date = {2020},
- keywords = {Fitness trackers, Global health, Internet of Things, Monitoring, physiologic, Telemedicine},
+ keywords = {Internet of Things, Fitness trackers, Global health, Monitoring, physiologic, Telemedicine},
file = {Full Text:/home/ulinja/Zotero/storage/2F69NQX4/Dagan and Mechanic - 2020 - Use of ultra-low cost fitness trackers as clinical.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/EV9AC9P6/display.html:text/html},
}
@@ -69,7 +414,7 @@
editor = {Gupta, Deepak and Khanna, Ashish and Bhattacharyya, Siddhartha and Hassanien, Aboul Ella and Anand, Sameer and Jaiswal, Ajay},
date = {2021},
langid = {english},
- keywords = {Alert, Android application, Cloud computing, Health care, Internet of things, {IoT}, Vital parameters, Wristband},
+ keywords = {Cloud computing, Alert, Android application, Health care, Internet of things, {IoT}, Vital parameters, Wristband},
file = {Full Text PDF:/home/ulinja/Zotero/storage/H4IPCNUM/Phaltankar et al. - 2021 - CuraBand Health Monitoring and Warning System.pdf:application/pdf},
}
@@ -84,7 +429,7 @@
journaltitle = {Instrumentation Mesure Metrologie},
author = {Thippeswamy, V.S. and Shivakumaraswamy, P.M. and Chickaramanna, S.G. and Iyengar, V.M. and Das, A.P. and Sharma, A.},
date = {2021},
- keywords = {{ECG}, Heart rate, {ICU}, Internet of things, Real-time monitoring, {SpO}2, Vital signs},
+ keywords = {Real-time monitoring, {ECG}, {ICU}, Internet of things, Heart rate, {SpO}2, Vital signs},
file = {Full Text:/home/ulinja/Zotero/storage/8XZ7QJYE/Thippeswamy et al. - 2021 - Prototype development of continuous remote monitor.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/B7RR7ZAW/display.html:text/html},
}
@@ -97,7 +442,7 @@
booktitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})},
author = {Yeri, Vani and Shubhangi, D.C.},
date = {2020-07},
- keywords = {Arduino, Cloud computing, Health, {IoT}, Medical services, monitoring, Monitoring, patient, sensor, Temperature measurement, Temperature sensors, wireless, Wireless communication, Wireless sensor networks},
+ keywords = {Cloud computing, Medical services, Monitoring, {IoT}, Arduino, Health, monitoring, patient, sensor, Temperature measurement, Temperature sensors, wireless, Wireless communication, Wireless sensor networks},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/I86F2Q3I/9183194.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/FS73U9GZ/Yeri and Shubhangi - 2020 - IoT based Real Time Health Monitoring.pdf:application/pdf},
}
@@ -140,7 +485,7 @@
journaltitle = {International Journal of Nursing Studies},
author = {Downey, C.L. and Tahir, W. and Randell, R. and Brown, J.M. and Jayne, D.G.},
date = {2017},
- keywords = {Early warning scores, Limitations, Strengths, Systematic review, Vital signs},
+ keywords = {Vital signs, Early warning scores, Limitations, Strengths, Systematic review},
file = {Accepted Version:/home/ulinja/Zotero/storage/B4RXIEJI/Downey et al. - 2017 - Strengths and limitations of early warning scores.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/C4DPHSQ6/display.html:text/html},
}
@@ -159,7 +504,7 @@
date = {2023},
langid = {english},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/aas.14221},
- keywords = {continuous monitoring, {COVID}-19, deterioration, early warning score, hospital admission, patient safety},
+ keywords = {{COVID}-19, continuous monitoring, deterioration, early warning score, hospital admission, patient safety},
file = {Full Text PDF:/home/ulinja/Zotero/storage/P9XWRWXW/Grønbæk et al. - 2023 - Continuous monitoring is superior to manual measur.pdf:application/pdf},
}
@@ -173,7 +518,8 @@
abstract = {Patients infected with {SARS}-{CoV}-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild {COVID}-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9\%) with mild {COVID}-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 ({NEWS}2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p {\textless} 0.0001) and oxygen saturation (r = 0.87, p {\textless} 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index ({BI}), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily {BI} was linearly associated with respiratory tract viral load (p {\textless} 0.0001) and {NEWS}2 (r = 0.75, p {\textless} 0.001). {BI} was superior to {NEWS}2 in predicting clinical worsening events (sensitivity 94.1\% and specificity 88.9\%) and prolonged hospitalization (sensitivity 66.7\% and specificity 72.7\%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.},
pages = {4388},
number = {1},
- journaltitle = {Sci Rep},
+ journaltitle = {Scientific Reports},
+ shortjournal = {Sci Rep},
author = {Un, Ka-Chun and Wong, Chun-Ka and Lau, Yuk-Ming and Lee, Jeffrey Chun-Yin and Tam, Frankie Chor-Cheung and Lai, Wing-Hon and Lau, Yee-Man and Chen, Hao and Wibowo, Sandi and Zhang, Xiaozhu and Yan, Minghao and Wu, Esther and Chan, Soon-Chee and Lee, Sze-Ming and Chow, Augustine and Tong, Raymond Cheuk-Fung and Majmudar, Maulik D. and Rajput, Kuldeep Singh and Hung, Ivan Fan-Ngai and Siu, Chung-Wah},
urldate = {2023-04-26},
date = {2021-02-23},
@@ -200,7 +546,7 @@ Publisher: Nature Publishing Group},
pmid = {30580650},
note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/17434440.2019.1563480},
- keywords = {Continuous monitoring, hospital, patient deterioration, vital signs, ward patients, wearable sensors},
+ keywords = {patient deterioration, Continuous monitoring, hospital, vital signs, ward patients, wearable sensors},
}
@article{downey_patient_2018,
@@ -220,11 +566,12 @@ Conclusion
Early warning score systems are widely used to facilitate detection of the deteriorating patient. Continuous monitoring technologies may provide added reassurance. However, patients value personal contact with their healthcare professionals and remote monitoring should not replace this. We suggest that remote monitoring is best introduced in a phased manner, and initially as an adjunct to usual care, with careful consideration of the patient experience throughout.},
pages = {52--56},
journaltitle = {International Journal of Medical Informatics},
+ shortjournal = {International Journal of Medical Informatics},
author = {Downey, C. L. and Brown, J. M. and Jayne, D. G. and Randell, R.},
urldate = {2023-04-26},
date = {2018-06-01},
langid = {english},
- keywords = {Early warning scores, Interviews, Monitoring, Patient experience, Vital signs},
+ keywords = {Monitoring, Vital signs, Early warning scores, Interviews, Patient experience},
file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/BBCZQB5R/S1386505618302508.html:text/html;Submitted Version:/home/ulinja/Zotero/storage/AL4WYTXJ/Downey et al. - 2018 - Patient attitudes towards remote continuous vital .pdf:application/pdf},
}
@@ -237,12 +584,13 @@ Early warning score systems are widely used to facilitate detection of the deter
abstract = {Continuous vital signs monitoring in post-surgical ward patients may support early detection of clinical deterioration, but novel alarm approaches are required to ensure timely notification of abnormalities and prevent alarm-fatigue. The current study explored the performance of classical and various adaptive threshold-based alarm strategies to warn for vital sign abnormalities observed during development of an adverse event. A classical threshold-based alarm strategy used for continuous vital signs monitoring in surgical ward patients was evaluated retrospectively. Next, (combinations of) six methods to adapt alarm thresholds to personal or situational factors were simulated in the same dataset. Alarm performance was assessed using the overall alarm rate and sensitivity to detect adverse events. Using a wireless patch-based monitoring system, 3999 h of vital signs data was obtained in 39 patients. The clinically used classical alarm system produced 0.49 alarms/patient/day, and alarms were generated for 11 out of 18 observed adverse events. Each of the tested adaptive strategies either increased sensitivity to detect adverse events or reduced overall alarm rate. Combining specific strategies improved overall performance most and resulted in earlier presentation of alarms in case of adverse events. Strategies that adapt vital sign alarm thresholds to personal or situational factors may improve early detection of adverse events or reduce alarm rates as compared to classical alarm strategies. Accordingly, further investigation of the potential of adaptive alarms for continuous vital signs monitoring in ward patients is warranted.},
pages = {407--417},
number = {2},
- journaltitle = {J Clin Monit Comput},
+ journaltitle = {Journal of Clinical Monitoring and Computing},
+ shortjournal = {J Clin Monit Comput},
author = {van Rossum, Mathilde C. and Vlaskamp, Lyan B. and Posthuma, Linda M. and Visscher, Maarten J. and Breteler, Martine J. M. and Hermens, Hermie J. and Kalkman, Cor J. and Preckel, Benedikt},
urldate = {2023-04-26},
date = {2022-04-01},
langid = {english},
- keywords = {Clinical alarms, Clinical deterioration, Physiological monitoring, Telemonitoring, Vital signs},
+ keywords = {Vital signs, Clinical alarms, Clinical deterioration, Physiological monitoring, Telemonitoring},
file = {Full Text PDF:/home/ulinja/Zotero/storage/V3VSFEIQ/van Rossum et al. - 2022 - Adaptive threshold-based alarm strategies for cont.pdf:application/pdf},
}
@@ -262,7 +610,7 @@ Early warning score systems are widely used to facilitate detection of the deter
pmid = {32212979},
note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/13696998.2020.1747474},
- keywords = {continuous monitoring, cost-effectiveness analysis, D70, H51, {SensiumVitals}, surgical patients, vital signs},
+ keywords = {continuous monitoring, vital signs, cost-effectiveness analysis, D70, H51, {SensiumVitals}, surgical patients},
file = {Full Text PDF:/home/ulinja/Zotero/storage/ZZ7Q5R9K/Javanbakht et al. - 2020 - Cost utility analysis of continuous and intermitte.pdf:application/pdf},
}
@@ -281,6 +629,7 @@ Antecedentes: Los pacientes con paro cardı́aco no esperado intrahospitalario t
pages = {137--141},
number = {2},
journaltitle = {Resuscitation},
+ shortjournal = {Resuscitation},
author = {Buist, Michael and Bernard, Stephen and Nguyen, Tuan V and Moore, Gaye and Anderson, Jeremy},
urldate = {2023-04-26},
date = {2004-08-01},
@@ -300,12 +649,13 @@ Antecedentes: Los pacientes con paro cardı́aco no esperado intrahospitalario t
pages = {e0210875},
number = {1},
journaltitle = {{PLOS} {ONE}},
+ shortjournal = {{PLOS} {ONE}},
author = {Brekke, Idar Johan and Puntervoll, Lars Håland and Pedersen, Peter Bank and Kellett, John and Brabrand, Mikkel},
urldate = {2023-04-26},
date = {2019-01-15},
langid = {english},
note = {Publisher: Public Library of Science},
- keywords = {Blood pressure, Cardiac arrest, Cohort studies, Heart rate, Medical risk factors, Oxygen, Respiration, Systematic reviews},
+ keywords = {Heart rate, Cardiac arrest, Blood pressure, Cohort studies, Medical risk factors, Oxygen, Respiration, Systematic reviews},
file = {Full Text PDF:/home/ulinja/Zotero/storage/5VV8R3MF/Brekke et al. - 2019 - The value of vital sign trends in predicting and m.pdf:application/pdf},
}
@@ -349,7 +699,7 @@ Antecedentes: Los pacientes con paro cardı́aco no esperado intrahospitalario t
langid = {english},
note = {Number: 22
Publisher: Multidisciplinary Digital Publishing Institute},
- keywords = {early warning score, {kNN}-{LS}-{SVM}, time-series prediction, vital signs, wearable technology},
+ keywords = {early warning score, vital signs, {kNN}-{LS}-{SVM}, time-series prediction, wearable technology},
file = {Full Text PDF:/home/ulinja/Zotero/storage/FAEVF9FC/Youssef Ali Amer et al. - 2020 - Vital Signs Prediction and Early Warning Score Cal.pdf:application/pdf},
}
@@ -379,11 +729,11 @@ Publisher: Multidisciplinary Digital Publishing Institute},
langid = {english},
note = {Number: 9
Publisher: Multidisciplinary Digital Publishing Institute},
- keywords = {{ECG}, edge computing, heartbeat detection, {IoT}, mobile healthcare, {QRS} detection, wearable device},
+ keywords = {{ECG}, {IoT}, edge computing, heartbeat detection, mobile healthcare, {QRS} detection, wearable device},
file = {Full Text PDF:/home/ulinja/Zotero/storage/TAYBJYZT/Chen and Chuang - 2017 - A QRS Detection and R Point Recognition Method for.pdf:application/pdf},
}
-@inproceedings{zarabzadeh_features_2012,
+@inproceedings{zarabzadeh_features_2012-1,
title = {Features of electronic Early Warning systems which impact clinical decision making},
doi = {10.1109/CBMS.2012.6266394},
abstract = {Paper-based Modified Early Warning Scorecards ({MEWS}) have been developed to help nursing staff detect hospital in-patient deterioration at an early stage. {MEWS} is based on patient vital signs where these values are transformed into a {MEWS} score. An electronic Modified Early Warning Scorecard ({eMEWS}) prototype has been designed and developed to fulfill the role of a computerized Clinical Decision Support System ({CDSS}) and to assist healthcare professionals in their decision making activities. A review of the existing electronic Early Warning Scorecards ({eEWS}) revealed they lack certain features that assist in capturing a holistic view of the patient health status for example color codes and vital sign trends. The proposed {eMEWS} prototype employs these features with the aim of assisting healthcare professionals to obtain a clear understanding of the patient status. A survey was conducted to evaluate the impact of paper-based {MEWS} and {eMEWS} as part of the decision making process. The advantages and disadvantages of {eMEWS} over the paper-based {MEWS} are presented.},
@@ -406,12 +756,13 @@ Publisher: Multidisciplinary Digital Publishing Institute},
abstract = {The most burning issues worldwide at present are the availability, accessibility, and affordability of the equitable healthcare services for all. It is getting more severe for developing countries due to increasing population and chronic diseases. The emerging technological interventions in the field of Internet of Things ({IoT})-based healthcare systems are a promising solution to meet the general public's healthcare needs. Therefore, an {IoT}-enabled vital sign monitoring system has been presented in this paper. The presented system can monitor various vital signs in real-time and store the recorded trends locally. The system can also send the data into cloud for further analysis. Abnormality detection with alert notification and automatic calculation of early warning score has been implemented. An Android application is developed to store the vital signs records on a personal server to avoid the burden and maintenance cost of the central medical server. The presented system is straightforward, compact, portable and easy to operate through personal service application. Also, the presented system is compared with the most recent work available in the field.},
pages = {129--156},
number = {1},
- journaltitle = {Wireless Pers Commun},
+ journaltitle = {Wireless Personal Communications},
+ shortjournal = {Wireless Pers Commun},
author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar},
urldate = {2023-04-26},
date = {2022-01-01},
langid = {english},
- keywords = {Abnormality detection, Alert notification, Healthcare, Internet of thing, Mobile communication, Personal service application, Real-time monitoring, Vital sign monitoring},
+ keywords = {Real-time monitoring, Healthcare, Abnormality detection, Alert notification, Internet of thing, Mobile communication, Personal service application, Vital sign monitoring},
file = {Full Text PDF:/home/ulinja/Zotero/storage/XTBR4NVR/Sahu et al. - 2022 - Vital Sign Monitoring System for Healthcare Throug.pdf:application/pdf},
}
@@ -424,12 +775,13 @@ Publisher: Multidisciplinary Digital Publishing Institute},
abstract = {The availability, accessibility, and affordability of good healthcare services to remote, rural, and developing parts of the world is a major challenge. To resolve this dynamically growing issue of global importance, there is a need to devise an integrated and intelligent solution for the delivery of health monitoring services along with abnormality detection and alert notification. In this work, a remote patient monitoring system ({RPMS}) has been presented. Internet of things ({IoT}) and integrated cloud computing technologies are used for the implementation. The system can continuously measure different physiological parameters with the appropriate degree of accuracy required by medical standards. A mobile application has been developed for Android devices, which acts as a gateway between {RPMS} and the Cloud. The developed mobile application offers visualization and storage of physiological parameters locally as well as in Cloud along with real-time data transmission for remote monitoring and further analysis. In case of an abnormal event and emergency, the system can generate an alert notification to the local user and remote supervisor. The {RPMS} has been implemented and validated on the state-of-the-art patient monitoring system. A series of tests have been carried out to validate the system’s effectiveness and reliability for measuring different physiological parameters and its remote monitoring in real-time. In addition to this performance analysis of the cloud-based system for real-time data transmission has also been carried out.},
pages = {1894--1909},
number = {5},
- journaltitle = {Mobile Netw Appl},
+ journaltitle = {Mobile Networks and Applications},
+ shortjournal = {Mobile Netw Appl},
author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar},
urldate = {2023-04-26},
date = {2022-10-01},
langid = {english},
- keywords = {Abnormality detection, Alert Notification, Internet of thing, Mobile Communication, Remote patient monitoring},
+ keywords = {Remote patient monitoring, Abnormality detection, Internet of thing, Alert Notification, Mobile Communication},
file = {Full Text PDF:/home/ulinja/Zotero/storage/BUVVMQQ9/Sahu et al. - 2022 - Cloud-Based Remote Patient Monitoring System with .pdf:application/pdf},
}
@@ -489,11 +841,12 @@ Conclusions
Very low and high {EWS} are able to discriminate between patients who are not likely and those who are likely to deteriorate in the pre-hospital setting. No study compared outcomes pre- and post-implementation of {EWS} so there is no evidence on whether patient outcomes differ between pre-hospital settings that do and do not use {EWS}. Further studies are required to address this question and to evaluate {EWS} in pre-hospital settings.},
pages = {101--111},
journaltitle = {Resuscitation},
+ shortjournal = {Resuscitation},
author = {Patel, Rita and Nugawela, Manjula D. and Edwards, Hannah B. and Richards, Alison and Le Roux, Hein and Pullyblank, Anne and Whiting, Penny},
urldate = {2023-04-26},
date = {2018-11-01},
langid = {english},
- keywords = {Critical care, Deteriorating patients, Early warning score, Pre hospital setting, Track and trigger system},
+ keywords = {Early warning score, Deteriorating patients, Critical care, Pre hospital setting, Track and trigger system},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/NKPJTMTR/Patel et al. - 2018 - Can early warning scores identify deteriorating pa.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/P6WFVY87/S0300957218308190.html:text/html},
}
@@ -506,11 +859,12 @@ Very low and high {EWS} are able to discriminate between patients who are not li
abstract = {Due to the {COVID}-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients’ status. Internet of Things ({IoT}) technologies have been used for monitoring patients’ health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining {IoT}-related technologies with early-warning scores ({EWS}) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the {NEWS}-2 has been showing remarkable results in detecting the health deterioration of {COVID}-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for {COVID}-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive {IoT}-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of {COVID}-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with {EWS}.},
pages = {100399},
journaltitle = {Internet of Things},
+ shortjournal = {Internet of Things},
author = {Paganelli, Antonio Iyda and Velmovitsky, Pedro Elkind and Miranda, Pedro and Branco, Adriano and Alencar, Paulo and Cowan, Donald and Endler, Markus and Morita, Plinio Pelegrini},
urldate = {2023-04-26},
date = {2022-05-01},
langid = {english},
- keywords = {Architecture, Consent, {COVID}-19, {IoT}, {NEWS}-2, Remote monitoring},
+ keywords = {{COVID}-19, {IoT}, Architecture, Consent, {NEWS}-2, Remote monitoring},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/GYESA337/Paganelli et al. - 2022 - A conceptual IoT-based early-warning architecture .pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/VNA4PMAC/S2542660521000433.html:text/html},
}
@@ -525,7 +879,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
author = {Tiwari, Divyanshu and Prasad, Devendra and Guleria, Kalpna and Ghosh, Pinaki},
date = {2021-10},
note = {{ISSN}: 2643-8615},
- keywords = {Biomedical monitoring, Costs, Health monitoring, healthcare, heart monitoring devices, {IoT}, medical services, Medical services, Monitoring, Remote monitoring, Signal processing},
+ keywords = {Biomedical monitoring, Medical services, Monitoring, {IoT}, Remote monitoring, Costs, Health monitoring, healthcare, heart monitoring devices, medical services, Signal processing},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GTHWZ2L3/9609393.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/NANQYIU6/Tiwari et al. - 2021 - IoT based Smart Healthcare Monitoring Systems A R.pdf:application/pdf},
}
@@ -556,7 +910,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
editor = {R, Shriram and Sharma, Mak},
date = {2018},
langid = {english},
- keywords = {Diverse emergency situation, Health monitoring, {IoT}, Tele-medicine},
+ keywords = {{IoT}, Health monitoring, Diverse emergency situation, Tele-medicine},
}
@inproceedings{karvounis_hospital_2021,
@@ -568,7 +922,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference ({SEEDA}-{CECNSM})},
author = {Karvounis, Evaggelos and Vavva, Maria and Giannakeas, Nikolaos and Tzallas, Alexandros T. and Smanis, Ioannis and Tsipouras, Markos G.},
date = {2021-09},
- keywords = {Artificial Intelligence ({AI}), component, Electronic healthcare, health monitoring, Hospitals, Internet of Things, Sensor systems, Smart healthcare, Temperature measurement, Temperature sensors, ubiquitous computing, wearable devices, Wireless communication, Wireless Sensor Network, Wireless sensor networks},
+ keywords = {Internet of Things, Wireless Sensor Network, Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks, Hospitals, Artificial Intelligence ({AI}), component, Electronic healthcare, health monitoring, Sensor systems, Smart healthcare, ubiquitous computing, wearable devices},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/IDTXE2ZS/9566252.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/S2WR9JTE/Karvounis et al. - 2021 - A Hospital Healthcare Monitoring System Using Inte.pdf:application/pdf},
}
@@ -607,7 +961,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
date = {2022-08-15},
note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/03772063.2022.2110528},
- keywords = {Automated {EWS}, Early warning score, In-home system, Internet of things, Physiological parameters, Sensors},
+ keywords = {Early warning score, Sensors, Physiological parameters, Internet of things, Automated {EWS}, In-home system},
file = {Full Text PDF:/home/ulinja/Zotero/storage/2JFXM2RX/Sahu et al. - 2022 - Internet-of-Things-Enabled Early Warning Score Sys.pdf:application/pdf},
}
@@ -627,7 +981,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
date = {2023},
langid = {english},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1485},
- keywords = {artificial intelligence, {IoT}, noninvasive technology, remote patient monitoring},
+ keywords = {{IoT}, artificial intelligence, noninvasive technology, remote patient monitoring},
file = {Full Text PDF:/home/ulinja/Zotero/storage/WUD6AIM4/Shaik et al. - 2023 - Remote patient monitoring using artificial intelli.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/JUM4HJDC/widm.html:text/html},
}
@@ -640,7 +994,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2021 International Conference on Technological Advancements and Innovations ({ICTAI})},
author = {Quraishi, Suhail Javed and Yusuf, Humra},
date = {2021-11},
- keywords = {Bibliographies, Healthcare, Information technologies, Inspection, Internet of Things, {IoT}, Medical services, Real-time systems, Remote inspection, Sensors, Smart devices, Technological innovation},
+ keywords = {Internet of Things, Healthcare, Medical services, Sensors, {IoT}, Bibliographies, Information technologies, Inspection, Real-time systems, Remote inspection, Smart devices, Technological innovation},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/KZGMR5L4/9673369.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/KR2C82FM/Quraishi and Yusuf - 2021 - Internet of Things in Healthcare, A Literature Rev.pdf:application/pdf},
}
@@ -653,7 +1007,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2022 International Conference on Advanced Computing Technologies and Applications ({ICACTA})},
author = {B V, Santhosh Krishna and Sharma, Sanjeev and Swathi, Kurapati Sai and Yamini, Korapati Reddy and Kiran, Chokkam Preethi and Chandrika, Kamineni},
date = {2022-03},
- keywords = {Diagnosis, Electrocardiography, Encryption, Heart, Internet of Things, Internet of Things [{IoT}], Medical services, Monitoring, Perception, Productivity, Security},
+ keywords = {Internet of Things, Medical services, Monitoring, Security, Diagnosis, Electrocardiography, Encryption, Heart, Internet of Things [{IoT}], Perception, Productivity},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/MY7DTWBQ/9753547.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/3VPC4T36/B V et al. - 2022 - Review on IoT based Healthcare systems.pdf:application/pdf},
}
@@ -666,7 +1020,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2020 International Symposium on Networks, Computers and Communications ({ISNCC})},
author = {de Mello Dantas, Hugo and Miceli de Farias, Claudio},
date = {2020-10},
- keywords = {Biomedical monitoring, Data integration, Emergency Detection, Internet of Things, Medical services, Monitoring, Remote Health Monitoring, Uncertainty, Wireless Body Sensor Networks, Wireless communication},
+ keywords = {Internet of Things, Biomedical monitoring, Medical services, Monitoring, Wireless communication, Data integration, Emergency Detection, Remote Health Monitoring, Uncertainty, Wireless Body Sensor Networks},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/ZPAGY7ER/9297315.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/EIHD9N7X/de Mello Dantas and Miceli de Farias - 2020 - A data fusion algorithm for clinically relevant an.pdf:application/pdf},
}
@@ -684,7 +1038,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
editor = {Mandler, Benny and Marquez-Barja, Johann and Mitre Campista, Miguel Elias and Cagáňová, Dagmar and Chaouchi, Hakima and Zeadally, Sherali and Badra, Mohamad and Giordano, Stefano and Fazio, Maria and Somov, Andrey and Vieriu, Radu-Laurentiu},
date = {2016},
langid = {english},
- keywords = {e-Health, Early warning score, Internet-of-Things, Remote patient monitoring},
+ keywords = {Early warning score, Remote patient monitoring, Internet-of-Things, e-Health},
}
@article{gomez_patient_2016,
@@ -697,11 +1051,12 @@ Very low and high {EWS} are able to discriminate between patients who are not li
abstract = {The increased use of mobile technologies and smart devices in the area of health has caused great impact on the world. Health experts are increasingly taking advantage of the benefits these technologies bring, thus generating a significant improvement in health care in clinical settings and out of them. Likewise, countless ordinary users are being served from the advantages of the M-Health (Mobile Health) applications and E-Health (health care supported by {ICT}) to improve, help and assist their health. Applications that have had a major refuge for these users, so intuitive environment. The Internet of things is increasingly allowing to integrate devices capable of connecting to the Internet and provide information on the state of health of patients and provide information in real time to doctors who assist. It is clear that chronic diseases such as diabetes, heart and pressure among others, are remarkable in the world economic and social level problem. The aim of this article is to develop an architecture based on an ontology capable of monitoring the health and workout routine recommendations to patients with chronic diseases.},
pages = {90--97},
journaltitle = {Procedia Computer Science},
+ shortjournal = {Procedia Computer Science},
author = {Gómez, Jorge and Oviedo, Byron and Zhuma, Emilio},
urldate = {2023-04-26},
date = {2016-01-01},
langid = {english},
- keywords = {Context Awareness, E-Health, Internet of Things, Ontology},
+ keywords = {Internet of Things, E-Health, Context Awareness, Ontology},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/LDZVGYUT/Gómez et al. - 2016 - Patient Monitoring System Based on Internet of Thi.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/XH3BU4JZ/S1877050916301260.html:text/html},
}
@@ -714,7 +1069,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2016 17th International Carpathian Control Conference ({ICCC})},
author = {Archip, Alexandru and Botezatu, Nicolae and Şerban, Elena and Herghelegiu, Paul-Corneliu and Zală, Andrei},
date = {2016-05},
- keywords = {Biomedical monitoring, E-health, Electrocardiography, Embedded Systems, Internet of things, Internet of Things, Logic gates, Monitoring, Prototypes, Remote patient monitoring, {RESTful} Web Services, Temperature sensors},
+ keywords = {Internet of Things, Biomedical monitoring, Monitoring, Remote patient monitoring, Internet of things, Temperature sensors, Prototypes, Electrocardiography, E-health, Embedded Systems, Logic gates, {RESTful} Web Services},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GR5KW752/7501056.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/SJIJNI7I/Archip et al. - 2016 - An IoT based system for remote patient monitoring.pdf:application/pdf},
}
@@ -727,7 +1082,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2018 2nd International Conference on 2018 2nd International Conference on I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})},
author = {Chowdary, Kovuru Chandu and Lokesh Krishna, K. and Prasad, K Lalu and Thejesh, K.},
date = {2018-08},
- keywords = {and {IoT}, Biomedical monitoring, Blood flow rate, Blood pressure, {GSM}, Medical services, Microcontroller, Monitoring, Remote monitoring, Temperature measurement, temperature Sensor node, Temperature sensors},
+ keywords = {Biomedical monitoring, Medical services, Monitoring, Temperature measurement, Temperature sensors, Blood pressure, Remote monitoring, and {IoT}, Blood flow rate, {GSM}, Microcontroller, temperature Sensor node},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/AW9IDIT6/8653716.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/GCRHYVX8/Chowdary et al. - 2018 - An Efficient Wireless Health Monitoring System.pdf:application/pdf},
}
@@ -741,19 +1096,10 @@ Very low and high {EWS} are able to discriminate between patients who are not li
author = {Athira, A. and Devika, T.D. and Varsha, K.R. and Bose S., Sree Sanjanaa},
date = {2020-03},
note = {{ISSN}: 2575-7288},
- keywords = {Biomedical monitoring, Heart rate, {IOT}, Medical services, Monitoring, {MPM}, Smart Health, {SVM} classifier, Temperature measurement, Temperature sensors},
+ keywords = {Biomedical monitoring, Medical services, Monitoring, Heart rate, Temperature measurement, Temperature sensors, {IOT}, {MPM}, Smart Health, {SVM} classifier},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/3CAVK8H5/9074293.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/Y852V2DN/Athira et al. - 2020 - Design and Development of IOT Based Multi-Paramete.pdf:application/pdf},
}
-@online{zarabzadeh_features_2012-1,
- title = {Features of electronic Early Warning systems which impact clinical decision making {\textbar} {IEEE} Conference Publication {\textbar} {IEEE} Xplore},
- url = {https://ieeexplore.ieee.org/document/6266394},
- author = {Zarabzadeh, Atieh},
- urldate = {2023-04-26},
- date = {2012},
- file = {Features of electronic Early Warning systems which impact clinical decision making | IEEE Conference Publication | IEEE Xplore:/home/ulinja/Zotero/storage/Q9BI6RWR/6266394.html:text/html;Features of electronic Early Warning systems which.pdf:/home/ulinja/Zotero/storage/SSHGFSTF/Features of electronic Early Warning systems which.pdf:application/pdf},
-}
-
@article{anzanpour_internet_2015-1,
title = {Internet of Things Enabled In-Home Health Monitoring System Using Early Warning Score},
volume = {"2"},
@@ -778,7 +1124,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2016 International Workshop on Big Data and Information Security ({IWBIS})},
author = {Azimi, Iman and Anzanpour, Arman and Rahmani, Amir M. and Liljeberg, Pasi and Salakoski, Tapio},
date = {2016-10},
- keywords = {Autonomic computing, Biomedical monitoring, Cloud computing, Computer architecture, Electrocardiography, Fog Comouting, Internet of Things, Logic gates, machine learning, Patient monitoring},
+ keywords = {Internet of Things, Biomedical monitoring, Cloud computing, Electrocardiography, Logic gates, Autonomic computing, Computer architecture, Fog Comouting, machine learning, Patient monitoring},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/NCEXJGHU/7872884.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/B4P7DF44/Azimi et al. - 2016 - Medical warning system based on Internet of Things.pdf:application/pdf},
}
@@ -791,331 +1137,53 @@ Very low and high {EWS} are able to discriminate between patients who are not li
booktitle = {2014 International Conference and Exposition on Electrical and Power Engineering ({EPE})},
author = {Chiuchisan, Iuliana and Costin, Hariton-Nicolae and Geman, Oana},
date = {2014-10},
- keywords = {Biomedical monitoring, health care system, internet of things, Internet of Things, Kinect, Medical services, Monitoring, sensors, smart environment, Temperature measurement, Temperature sensors},
+ keywords = {Internet of Things, Biomedical monitoring, Medical services, Monitoring, Temperature measurement, Temperature sensors, health care system, internet of things, Kinect, sensors, smart environment},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GC7RHLL2/6969965.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/8CZGFIAN/Chiuchisan et al. - 2014 - Adopting the Internet of Things technologies in he.pdf:application/pdf},
}
-@article{da_silva_deepsigns_2021,
- title = {{DeepSigns}: A predictive model based on Deep Learning for the early detection of patient health deterioration},
- volume = {165},
- issn = {0957-4174},
- url = {https://www.sciencedirect.com/science/article/pii/S0957417420307004},
- doi = {10.1016/j.eswa.2020.113905},
- shorttitle = {{DeepSigns}},
- abstract = {Early diagnosis of critically ill patients depends on the attention and observation of medical staff about different variables, as vital signs, results of laboratory tests, among other. Seriously ill patients usually have changes in their vital signs before worsening. Monitoring these changes is important to anticipate the diagnosis in order to initiate patients’ care. Prognostic indexes play a fundamental role in this context since they allow to estimate the patients’ health status. Besides, the adoption of electronic health records improved the availability of data, which can be processed by machine learning techniques for information extraction to support clinical decisions. In this context, this work aims to create a computational model able to predict the deterioration of patients’ health status in such a way that it is possible to start the appropriate treatment as soon as possible. The model was developed based on Deep Learning technique, a Recurrent Neural Networks, the Long Short-Term Memory, for the prediction of patient’s vital signs and subsequent evaluation of the patient’s health status severity through Prognostic Indexes commonly used in the health area. Experiments showed that it is possible to predict vital signs with good precision (accuracy {\textgreater} 80\%) and, consequently, predict the Prognostic Indexes in advance to treat the patients before deterioration. Predicting the patient’s vital signs for the future and use them for the Prognostic Index’ calculation allows clinical times to predict future severe diagnoses that would not be possible applying the current patient’s vital signs (50\%–60\% of cases would not be identified).},
- pages = {113905},
- journaltitle = {Expert Systems with Applications},
- author = {da Silva, Denise Bandeira and Schmidt, Diogo and da Costa, Cristiano André and da Rosa Righi, Rodrigo and Eskofier, Björn},
- urldate = {2023-04-27},
- date = {2021-03-01},
+@report{hardt_oauth_2012,
+ title = {The {OAuth} 2.0 Authorization Framework},
+ url = {https://datatracker.ietf.org/doc/rfc6749},
+ abstract = {The {OAuth} 2.0 authorization framework enables a third-party application to obtain limited access to an {HTTP} service, either on behalf of a resource owner by orchestrating an approval interaction between the resource owner and the {HTTP} service, or by allowing the third-party application to obtain access on its own behalf. This specification replaces and obsoletes the {OAuth} 1.0 protocol described in {RFC} 5849. [{STANDARDS}-{TRACK}]},
+ number = {{RFC} 6749},
+ institution = {Internet Engineering Task Force},
+ type = {Request for Comments},
+ author = {Hardt, Dick},
+ urldate = {2023-08-21},
+ date = {2012-10},
+ doi = {10.17487/RFC6749},
+ note = {Num Pages: 76},
+ file = {Full Text PDF:/home/ulinja/Zotero/storage/978WTBV3/Hardt - 2012 - The OAuth 2.0 Authorization Framework.pdf:application/pdf},
+}
+
+@online{noauthor_gotify_nodate,
+ title = {Gotify · a simple server for sending and receiving messages},
+ url = {https://gotify.net/},
+ abstract = {a simple server for sending and receiving messages},
+ urldate = {2023-08-21},
+ file = {Snapshot:/home/ulinja/Zotero/storage/7IW3JUKM/gotify.net.html:text/html},
+}
+
+@online{noauthor_keep_nodate,
+ title = {Keep user's data up to date {\textbar} Withings},
+ url = {https://developer.withings.com/developer-guide/v3/integration-guide/public-health-data-api/data-api/keep-user-data-up-to-date},
+ abstract = {In order for your services to always be up to date with your program members' data, Withings {API} includes a data notification system.},
+ urldate = {2023-08-22},
langid = {english},
- keywords = {Deep learning, Health informatics, {LSTM}, Machine learning, Predictive scores},
- file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/FDRS6GKT/S0957417420307004.html:text/html},
+ file = {Snapshot:/home/ulinja/Zotero/storage/VKTHB7RR/keep-user-data-up-to-date.html:text/html},
}
-@article{burgos-esteban_effectiveness_2022,
- title = {Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review},
- volume = {10},
- rights = {cc by},
- issn = {2296-2565},
- url = {https://europepmc.org/articles/PMC9330632},
- doi = {10.3389/fpubh.2022.894906},
- shorttitle = {Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care},
- abstract = {Background and {objectivesPatient} assessment and possible deterioration prediction are a healthcare priority. Increasing demand for outpatient emergency care services requires the implementation of simple, quick, and effective systems of patient evaluation and stratification. The purpose of this review is to identify the most effective Early Warning Score ({EWS}) for the early detection of the risk of complications when screening emergency outpatients for a potentially serious condition.Materials and {methodsSystematic} review of the bibliography made in 2022. Scientific articles in Spanish and English were collected from the databases and search engines of Pubmed, Cochrane, and Dialnet, which were published between 2017 and 2021 about {EWSs} and their capacity to predict complications.{ResultsFor} analysis eleven articles were selected. Eight dealt with the application of different early warning scores in outpatient situations, concluding that all the scoring systems they studied were applicable. Three evaluated the predictive ability of various scoring systems and found no significant differences in their results. The eight articles evaluated the suitability of {NEWS}/{NEWS}2 to outpatient conditions and concluded it was the most suitable in pre-hospital emergency settings.{ConclusionsThe} early warning scores that were studied can be applied at the pre-hospital level, as they can predict patient mortality in the short term (24 or 48 h) and support clinical patient evaluation and medical decision making. Among them, {NEWS}2 is the most suitable for screening potentially deteriorating medical emergency outpatients.},
- pages = {894906},
- journaltitle = {Front Public Health},
- author = {Burgos-Esteban, Amaya and Gea-Caballero, Vicente and Marín-Maicas, Patricia and Santillán-García, Azucena and Cordón-Hurtado, María de Valvanera and Marqués-Sule, Elena and Giménez-Luzuriaga, Marta and Juárez-Vela, Raúl and Sanchez-Gonzalez, Juan Luis and García-Criado, Jorge and Santolalla-Arnedo, Iván},
- urldate = {2023-04-27},
- date = {2022-01-01},
- pmid = {35910902},
- pmcid = {PMC9330632},
- keywords = {Emergency Care, Emergency Medical Service (Ems), Emergency Medicine, Medicine, Scale},
- file = {Full Text PDF (Open access):/home/ulinja/Zotero/storage/NFFHLGDV/Burgos-Esteban et al. - 2022 - Effectiveness of Early Warning Scores for Early Se.pdf:application/pdf},
-}
-
-@article{alam_exploring_2015,
- title = {Exploring the performance of the National Early Warning Score ({NEWS}) in a European emergency department},
- volume = {90},
- issn = {0300-9572},
- url = {https://www.sciencedirect.com/science/article/pii/S0300957215000787},
- doi = {10.1016/j.resuscitation.2015.02.011},
- abstract = {Background
-Several triage systems have been developed for use in the emergency department ({ED}), however they are not designed to detect deterioration in patients. Deteriorating patients may be at risk of going undetected during their {ED} stay and are therefore vulnerable to develop serious adverse events ({SAEs}). The National Early Warning Score ({NEWS}) has a good ability to discriminate ward patients at risk of {SAEs}. The utility of {NEWS} had not yet been studied in an {ED}.
-Objective
-To explore the performance of the {NEWS} in an {ED} with regard to predicting adverse outcomes.
-Design
-A prospective observational study. Patients Eligible patients were those presenting to the {ED} during the 6 week study period with an Emergency Severity Index ({ESI}) of 2 and 3 not triaged to the resuscitation room.
-Intervention
-{NEWS} was documented at three time points: on arrival (T0), hour after arrival (T1) and at transfer to the general ward/{ICU} (T2). The outcomes of interest were: hospital admission, {ICU} admission, length of stay and 30 day mortality.
-Results
-A total of 300 patients were assessed for eligibility. Complete data was able to be collected for 274 patients on arrival at the {ED}. {NEWS} was significantly correlated with patient outcomes, including 30 day mortality, hospital admission, and length of stay at all-time points.
-Conclusion
-The {NEWS} measured at different time points was a good predictor of patient outcomes and can be of additional value in the {ED} to longitudinally monitor patients throughout their stay in the {ED} and in the hospital.},
- pages = {111--115},
- journaltitle = {Resuscitation},
- author = {Alam, N. and Vegting, I. L. and Houben, E. and van Berkel, B. and Vaughan, L. and Kramer, M. H. H. and Nanayakkara, P. W. B.},
- urldate = {2023-04-27},
- date = {2015-05-01},
- langid = {english},
- keywords = {Clinical outcomes, Deteriorating patients, Early warning score, Monitoring, {NEWS}, Physiological parameters},
- file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/HI4XZEPG/S0300957215000787.html:text/html},
-}
-
-@article{bilben_national_2016,
- title = {National Early Warning Score ({NEWS}) as an emergency department predictor of disease severity and 90-day survival in the acutely dyspneic patient – a prospective observational study},
- volume = {24},
- rights = {2016 The Author(s).},
- issn = {1757-7241},
- url = {https://sjtrem.biomedcentral.com/articles/10.1186/s13049-016-0273-9},
- doi = {10.1186/s13049-016-0273-9},
- abstract = {National Early Warning Score ({NEWS}) was designed to detect deteriorating patients in hospital wards, specifically those at increased risk of {ICU} admission, cardiac arrest, or death within 24 h. {NEWS} is not validated for use in Emergency Departments ({ED}), but emerging data suggest it may be useful. A criticism of {NEWS} is that patients with chronic poor oxygenation, e.g. severe chronic obstructive pulmonary disease ({COPD}), will have elevated {NEWS} also in the absence of acute deterioration, possibly reducing the predictive power of {NEWS} in this subgroup. We wanted to prospectively evaluate the usefulness of {NEWS} in unselected adult patients emergently presenting in a Norwegian {ED} with respiratory distress as main symptom. In respiratory distressed patients, {NEWS} was calculated on {ED} arrival, after 2–4 h, and the next day. Manchester Triage Scale ({MTS}) category, age, gender, comorbidity ({ASA} score), {ICU}-admission, ventilatory support, and discharge diagnoses were noted. Survival status was tracked for {\textgreater}90 days through the Population Registry. Data are medians (25–75th percentiles). Factors predicting 90-day survival were analysed with multiple logistic regression. We included 246 patients; 71 years old (60–80), 89 \% home-dwelling, 74 \% {ASA} 3–4, 72 \% {MTS} 1–2, 88 \% admitted to hospital. {NEWS} on arrival was 5 (3–7). {NEWS} correlated closely with {MTS} category and maximum in-hospital level of care ({ED}, ward, high-dependency unit, {ICU}). Sixteen patients died in-hospital, 26 died after discharge within 90 days. Controlled for age, {ASA} score, and {COPD}, a higher {NEWS} on {ED} arrival predicted poorer 90-day survival. Increased {NEWS} also correlated with decreased 30-day- and in-hospital survival and a decreased probability for home-dwelling patients to be discharged directly home. In respiratory distressed patients, {NEWS} on {ED} arrival correlated closely with triage category and need of {ICU} admission and predicted long-term out-of-hospital survival controlled for age, comorbidity, and {COPD}. {NEWS} should be explored in the {ED} setting to determine its role in clinical decision-making and in communication along the acute care chain.},
- pages = {1--8},
- number = {1},
- journaltitle = {Scand J Trauma Resusc Emerg Med},
- author = {Bilben, Bente and Grandal, Linda and Søvik, Signe},
- urldate = {2023-04-27},
- date = {2016-12},
- langid = {english},
- note = {Number: 1
-Publisher: {BioMed} Central},
- file = {Full Text PDF:/home/ulinja/Zotero/storage/YAGFBLNR/Bilben et al. - 2016 - National Early Warning Score (NEWS) as an emergenc.pdf:application/pdf},
-}
-
-@article{gidari_predictive_2020,
- title = {Predictive value of National Early Warning Score 2 ({NEWS}2) for intensive care unit admission in patients with {SARS}-{CoV}-2 infection},
- volume = {52},
- issn = {2374-4235},
- url = {https://doi.org/10.1080/23744235.2020.1784457},
- doi = {10.1080/23744235.2020.1784457},
- abstract = {Background: From January 2020, Coronavirus disease 19 ({COVID}-19) has rapidly spread all over the world. An early assessment of illness severity is important for the stratification of patients. We analysed the predictive value of National Early Warning Score 2 ({NEWS}2) for intensive care unit admission ({ICU}) in patients with Severe Acute Respiratory Syndrome- Coronavirus-2 ({SARS}-{CoV}-2) infection.Methods: Data of 71 patients with {SARS}-{CoV}-2 admitted from 1 March to 20 April 2020, to the Clinic of Infectious Diseases of Perugia Hospital, Italy, were retrospectively reviewed. {NEWS}2 at hospital admission, demographic, comorbidity and clinical data were collected. Univariate and multivariate analyses were performed to establish the correlation between each variable and {ICU} admission.Results: Among 68 patients included in the analysis, 27 were admitted to {ICU}. {NEWS}2 at hospital admission was a good predictor of {ICU} admission as shown by an area under the receiver-operating characteristic curve analysis of 0.90 (standard error 0.04; 95\% confidence interval 0.82–0.97). In multivariate logistic regression analysis, {NEWS}2 was significantly related to {ICU} admission using thresholds of 5 and 7. No other clinical variables included in the model were significantly correlated with {ICU} admission.A {NEWS}2 threshold of 5 had higher sensitivity than a threshold of 7 (89\% and 63\%). Higher specificity, positive likelihood ratio and positive predictive value were found using a threshold of 7 than a threshold of 5.Conclusions: {NEWS}2 at hospital admission was a good predictor for {ICU} admission. Patients with severe {COVID}-19 were correctly and rapidly stratified.},
- pages = {698--704},
- number = {10},
- journaltitle = {Infectious Diseases},
- author = {Gidari, Anna and De Socio, Giuseppe Vittorio and Sabbatini, Samuele and Francisci, Daniela},
- urldate = {2023-04-27},
- date = {2020-10-02},
- pmid = {32584161},
- note = {Publisher: Taylor \& Francis
-\_eprint: https://doi.org/10.1080/23744235.2020.1784457},
- keywords = {{COVID}-19, {ICU}, National Early Warning Score 2, {NEWS}2, {SARS}-{CoV}-2},
-}
-
-@article{otoom_iot-based_2020,
- title = {An {IoT}-based framework for early identification and monitoring of {COVID}-19 cases},
- volume = {62},
- issn = {1746-8094},
- url = {https://www.sciencedirect.com/science/article/pii/S1746809420302949},
- doi = {10.1016/j.bspc.2020.102149},
- abstract = {The world has been facing the challenge of {COVID}-19 since the end of 2019. It is expected that the world will need to battle the {COVID}-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time {COVID}-19 detection and monitoring system. The proposed system would employ an Internet of Things ({IoTs}) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine ({SVM}), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-{NN}), Decision Table, Decision Stump, {OneR}, and {ZeroR}. An experiment was conducted to test these eight algorithms on a real {COVID}-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 \%. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of {COVID}-19, and the framework would then document the treatment response for each patient who has contracted the virus.},
- pages = {102149},
- journaltitle = {Biomedical Signal Processing and Control},
- author = {Otoom, Mwaffaq and Otoum, Nesreen and Alzubaidi, Mohammad A. and Etoom, Yousef and Banihani, Rudaina},
- urldate = {2023-04-27},
- date = {2020-09-01},
- langid = {english},
- keywords = {Coronaviruses, {COVID}-19, Early identification or prediction, Internet of Things, Real-time monitoring, Treatment response},
- file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/NCS9RXIF/Otoom et al. - 2020 - An IoT-based framework for early identification an.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/ZS8ARH8Q/S1746809420302949.html:text/html},
-}
-
-@article{filho_iot-based_2021,
- title = {An {IoT}-Based Healthcare Platform for Patients in {ICU} Beds During the {COVID}-19 Outbreak},
- volume = {9},
- issn = {2169-3536},
- doi = {10.1109/ACCESS.2021.3058448},
- abstract = {There is a global concern with the escalating number of patients at hospitals caused mainly by population aging, chronic diseases, and recently by the {COVID}-19 outbreak. To smooth this challenge, {IoT} emerges as an encouraging paradigm because it provides the scalability required for this purpose, supporting continuous and reliable health monitoring on a global scale. Based on this context, an {IoT}-based healthcare platform to provide remote monitoring for patients in a critical situation was proposed in the authors’ previous works. Therefore, this paper aims to extend the platform by integrating wearable and unobtrusive sensors to monitor patients with coronavirus disease. Furthermore, we report a real deployment of our approach in an intensive care unit for {COVID}-19 patients in Brazil.},
- pages = {27262--27277},
- journaltitle = {{IEEE} Access},
- author = {Filho, Itamir de Morais Barroca and Aquino, Gibeon and Malaquias, Ramon Santos and Girão, Gustavo and Melo, Sávio Rennan Menêzes},
- date = {2021},
- note = {Conference Name: {IEEE} Access},
- keywords = {Biomedical monitoring, Cloud computing, {COVID}-19, Healthcare, Internet of Things, Medical services, Monitoring, platform, Protocols, remote monitoring, Sensors},
- file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/QJRQD4DV/9351912.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/Z47T3IBP/Filho et al. - 2021 - An IoT-Based Healthcare Platform for Patients in I.pdf:application/pdf},
-}
-
-@article{carr_evaluation_2021,
- title = {Evaluation and improvement of the National Early Warning Score ({NEWS}2) for {COVID}-19: a multi-hospital study},
- volume = {19},
- issn = {1741-7015},
- url = {https://doi.org/10.1186/s12916-020-01893-3},
- doi = {10.1186/s12916-020-01893-3},
- shorttitle = {Evaluation and improvement of the National Early Warning Score ({NEWS}2) for {COVID}-19},
- abstract = {The National Early Warning Score ({NEWS}2) is currently recommended in the {UK} for the risk stratification of {COVID}-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate {NEWS}2 for the prediction of severe {COVID}-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of {NEWS}2 alone for medium-term risk stratification.},
- pages = {23},
- number = {1},
- journaltitle = {{BMC} Med},
- author = {Carr, Ewan and Bendayan, Rebecca and Bean, Daniel and Stammers, Matt and Wang, Wenjuan and Zhang, Huayu and Searle, Thomas and Kraljevic, Zeljko and Shek, Anthony and Phan, Hang T. T. and Muruet, Walter and Gupta, Rishi K. and Shinton, Anthony J. and Wyatt, Mike and Shi, Ting and Zhang, Xin and Pickles, Andrew and Stahl, Daniel and Zakeri, Rosita and Noursadeghi, Mahdad and O’Gallagher, Kevin and Rogers, Matt and Folarin, Amos and Karwath, Andreas and Wickstrøm, Kristin E. and Köhn-Luque, Alvaro and Slater, Luke and Cardoso, Victor Roth and Bourdeaux, Christopher and Holten, Aleksander Rygh and Ball, Simon and {McWilliams}, Chris and Roguski, Lukasz and Borca, Florina and Batchelor, James and Amundsen, Erik Koldberg and Wu, Xiaodong and Gkoutos, Georgios V. and Sun, Jiaxing and Pinto, Ashwin and Guthrie, Bruce and Breen, Cormac and Douiri, Abdel and Wu, Honghan and Curcin, Vasa and Teo, James T. and Shah, Ajay M. and Dobson, Richard J. B.},
- urldate = {2023-04-27},
- date = {2021-01-21},
- langid = {english},
- keywords = {Blood parameters, {COVID}-19, {NEWS}2 score, Prediction model},
- file = {Full Text PDF:/home/ulinja/Zotero/storage/4RTVXPRT/Carr et al. - 2021 - Evaluation and improvement of the National Early W.pdf:application/pdf},
-}
-
-@online{noauthor_visi_nodate,
- title = {Visi Mobile - Patient Vital Signs Monitoring System {\textbar} Sotera Digital Health},
- url = {https://soteradigitalhealth.com},
- abstract = {Sotera Digital Health make continuous patient monitoring system as the new standard of care for step-down and/or general floor units.},
- urldate = {2023-04-27},
- langid = {english},
- file = {Snapshot:/home/ulinja/Zotero/storage/SKUUNC7F/soteradigitalhealth.com.html:text/html},
-}
-
-@online{noauthor_equivital_nodate,
- title = {Equivital {LifeMonitor} - Mobile vital signs monitor},
- url = {https://equivital.com/mobile-vital-signs-monitor},
- abstract = {Equivital’s {LifeMonitor} is a body worn sensor which measures {ECG}, heart rate, breathing rate, skin temperature, activity and body position.},
- titleaddon = {Equivital},
- urldate = {2023-04-27},
- langid = {british},
- file = {Snapshot:/home/ulinja/Zotero/storage/GBSYE3DG/mobile-vital-signs-monitor.html:text/html},
-}
-
-@online{noauthor_vitls_nodate,
- title = {Vitls Tego - Vitals monitoring device for infants},
- url = {https://www.vitlsinc.com/unique-features},
- abstract = {Our wearable medical device has tackled the downsides to current vital monitoring options and engineered the ultimate way to care for your patients without the hassle.},
- titleaddon = {Vitls},
- urldate = {2023-04-27},
- langid = {american},
- file = {Snapshot:/home/ulinja/Zotero/storage/K8NGCBH5/unique-features.html:text/html},
-}
-
-@online{noauthor_caretaker_nodate,
- title = {Caretaker Medical {VitalStream}},
- url = {https://caretakermedical.net/},
- abstract = {{VitalStream} is the new standard in wireless patient monitoring. The device is clinically validated and {FDA} cleared.},
- urldate = {2023-04-27},
- langid = {english},
- file = {Snapshot:/home/ulinja/Zotero/storage/UGJRJ7A4/caretakermedical.net.html:text/html},
-}
-
-@online{noauthor_medtronic_nodate,
- title = {Medtronic {BioButton} {\textbar} Multi-parameter Wearable},
- url = {https://www.medtronic.com/covidien/en-us/products/remote-monitoring/healthcast-intelligent-patient-manager/healthcast-biobutton-multi-parameter-wearable.html},
- urldate = {2023-04-27},
- file = {BioButton®* Multi-parameter Wearable | Medtronic:/home/ulinja/Zotero/storage/Z5TF3VAL/healthcast-biobutton-multi-parameter-wearable.html:text/html},
-}
-
-@article{wu_predicting_2021,
- title = {Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score ({MEWS}) and machine learning approach},
- volume = {9},
- issn = {2167-8359},
- url = {https://peerj.com/articles/11988},
- doi = {10.7717/peerj.11988},
- shorttitle = {Predicting in-hospital mortality in adult non-traumatic emergency department patients},
- abstract = {Background A feasible and accurate risk prediction systems for emergency department ({ED}) patients is urgently required. The Modified Early Warning Score ({MEWS}) is a wide-used tool to predict clinical outcomes in {ED}. Literatures showed that machine learning ({ML}) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a {ML} model to predict in-hospital morality of the adult non traumatic {ED} patients for different time stages, and comparing performance with other {ML} models and {MEWS}. Methods A retrospective observational cohort study was conducted in five Taiwan {EDs} including two tertiary medical centers and three regional hospitals. All consecutively adult ({\textgreater}17 years old) non-traumatic patients admit to {ED} during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. {MEWS} was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking {ML} model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic ({AUROC}) and the area under the precision and recall curve ({AUPRC}) as the comparative measures. Result After excluding 182,001 visits (7.46\%), study group was consisted of 24,37,326 {ED} visits. The dataset was split into 67\% training data and 33\% test data for {ML} model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the {AUROC} of {MEW} and {ML} mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking {ML} model outperform other {ML} model as well. For the prediction of in-hospital mortality over 48-hours, {AUPRC} performance of {MEWS} drop below 0.1, while the {AUPRC} of {ML} mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, {ML} model achieved statistically significant higher {AUROC} and {AUPRC} than {MEWS} (all P {\textless} 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of {AUROC} values between two model increases gradually (P {\textless} 0.001). Three {MEWS} thresholds (score {\textgreater}3, {\textgreater}4, and {\textgreater}5) were determined as baselines for comparison, {ML} mode consistently showed improved or equally performance in sensitivity, {PPV}, {NPV}, but not in specific. Conclusion Stacking {ML} methods improve predicted in-hospital mortality than {MEWS} in adult non-traumatic {ED} patients, especially in the prediction of delayed mortality.},
- pages = {e11988},
- journaltitle = {{PeerJ}},
- author = {Wu, Kuan-Han and Cheng, Fu-Jen and Tai, Hsiang-Ling and Wang, Jui-Cheng and Huang, Yii-Ting and Su, Chih-Min and Chang, Yun-Nan},
- urldate = {2023-04-28},
- date = {2021-08-24},
- langid = {english},
- note = {Publisher: {PeerJ} Inc.},
- file = {Full Text PDF:/home/ulinja/Zotero/storage/H2MPDP9A/Wu et al. - 2021 - Predicting in-hospital mortality in adult non-trau.pdf:application/pdf},
-}
-
-@article{martin-rodriguez_analysis_2019,
- title = {Analysis of the early warning score to detect critical or high-risk patients in the prehospital setting},
- volume = {14},
- issn = {1970-9366},
- url = {https://doi.org/10.1007/s11739-019-02026-2},
- doi = {10.1007/s11739-019-02026-2},
- abstract = {The early warning score can help to prevent, recognize and act at the first signs of clinical and physiological deterioration. The objective of this study is to evaluate different scales for use in the prehospital setting and to select the most relevant one by applicability and capacity to predict mortality in the first 48 h. A prospective longitudinal observational study was conducted in patients over 18 years of age who were treated by the advanced life support unit and transferred to the emergency department between April and July 2018. We analyzed demographic variables as well as the physiological parameters and clinical observations necessary to complement the {EWS}. Subsequently, each patient was followed up, considering their final diagnosis and mortality data. A total of 349 patients were included in our study. Early mortality before the first 48 h affected 27 patients (7.7\%). The scale with the best capacity to predict early mortality was the National Early Warning Score 2, with an area under the curve of 0.896 (95\% {CI} 0.82–0.97). The score with the lowest global classification error was 10 points with sensitivity of 81.5\% (95\% {CI} 62.7–92.1) and specificity of 88.5\% (95\% {CI} 84.5–91.6). The early warning score studied (except modified early warning score) shows no statistically significant differences between them; however, the National Early Warning Score 2 is the most used score internationally, validated at the prehospital scope and with a wide scientific literature that supports its use. The Prehospital Emergency Medical Services should include this scale among their operative elements to complement the structured and objective evaluation of the critical patient.},
- pages = {581--589},
- number = {4},
- journaltitle = {Intern Emerg Med},
- author = {Martín-Rodríguez, Francisco and Castro-Villamor, Miguel Ángel and del Pozo Vegas, Carlos and Martín-Conty, José Luis and Mayo-Iscar, Agustín and Delgado Benito, Juan Francisco and del Brio Ibañez, Pablo and Arnillas-Gómez, Pedro and Escudero-Cuadrillero, Carlos and López-Izquierdo, Raúl},
- urldate = {2023-04-28},
- date = {2019-06-01},
- langid = {english},
- keywords = {Clinical research, Early mortality, Early warning score, Prehospital care, Prognosis},
- file = {Full Text PDF:/home/ulinja/Zotero/storage/2LVIYDZR/Martín-Rodríguez et al. - 2019 - Analysis of the early warning score to detect crit.pdf:application/pdf},
-}
-
-@article{abbott_pre-hospital_2018,
- title = {Pre-hospital National Early Warning Score ({NEWS}) is associated with in-hospital mortality and critical care unit admission: A cohort study},
- volume = {27},
- issn = {2049-0801},
- url = {https://www.sciencedirect.com/science/article/pii/S2049080118300116},
- doi = {10.1016/j.amsu.2018.01.006},
- shorttitle = {Pre-hospital National Early Warning Score ({NEWS}) is associated with in-hospital mortality and critical care unit admission},
- abstract = {Background
-National Early Warning Score ({NEWS}) is increasingly used in {UK} hospitals. However, there is only limited evidence to support the use of pre-hospital early warning scores. We hypothesised that pre-hospital {NEWS} was associated with death or critical care escalation within the first 48 h of hospital stay.
-Methods
-Planned secondary analysis of a prospective cohort study at a single {UK} teaching hospital. Consecutive medical ward admissions over a 20-day period were included in the study. Data were collected from ambulance report forms, medical notes and electronic patient records. Pre-hospital {NEWS} was calculated retrospectively. The primary outcome was a composite of death or critical care unit escalation within 48 h of hospital admission. The secondary outcome was length of hospital stay.
-Results
-189 patients were included in the analysis. The median pre-hospital {NEWS} was 3 ({IQR} 1–5). 13 patients (6.9\%) died or were escalated to the critical care unit within 48 h of hospital admission. Pre-hospital {NEWS} was associated with death or critical care unit escalation ({OR}, 1.25; 95\% {CI}, 1.04–1.51; p = 0.02), but {NEWS} on admission to hospital was more strongly associated with this outcome ({OR}, 1.52; 95\% {CI}, 1.18–1.97, p {\textless} 0.01). Neither was associated with hospital length of stay.
-Conclusion
-Pre-hospital {NEWS} was associated with death or critical care unit escalation within 48 h of hospital admission. {NEWS} could be used by ambulance crews to assist in the early triage of patients requiring hospital treatment or rapid transport. Further cohort studies or trials in large samples are required before implementation.},
- pages = {17--21},
- journaltitle = {Annals of Medicine and Surgery},
- author = {Abbott, Tom E. F. and Cron, Nicholas and Vaid, Nidhi and Ip, Dorothy and Torrance, Hew D. T. and Emmanuel, Julian},
- urldate = {2023-04-28},
- date = {2018-03-01},
- langid = {english},
- keywords = {Acute care emergency ambulance systems, Clinical research, Intensive care, Pre-hospital},
- file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/2HPZCFXG/Abbott et al. - 2018 - Pre-hospital National Early Warning Score (NEWS) i.pdf:application/pdf},
-}
-
-@article{subbe_validation_2001,
- title = {Validation of a modified Early Warning Score in medical admissions},
- volume = {94},
- issn = {1460-2725},
- url = {https://doi.org/10.1093/qjmed/94.10.521},
- doi = {10.1093/qjmed/94.10.521},
- abstract = {The Early Warning Score ({EWS}) is a simple physiological scoring system suitable for bedside application. The ability of a modified Early Warning Score ({MEWS}) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated. In a prospective cohort study, we applied {MEWS} to patients admitted to the 56‐bed acute Medical Admissions Unit ({MAU}) of a District General Hospital ({DGH}). Data on 709 medical emergency admissions were collected during March 2000. Main outcome measures were death, intensive care unit ({ICU}) admission, high dependency unit ({HDU}) admission, cardiac arrest, survival and hospital discharge at 60 days. Scores of 5 or more were associated with increased risk of death ({OR} 5.4, 95\%{CI} 2.8–10.7), {ICU} admission ({OR} 10.9, 95\%{CI} 2.2–55.6) and {HDU} admission ({OR} 3.3, 95\%{CI} 1.2–9.2). {MEWS} can be applied easily in a {DGH} medical admission unit, and identifies patients at risk of deterioration who require increased levels of care in the {HDU} or {ICU}. A clinical pathway could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management.},
- pages = {521--526},
- number = {10},
- journaltitle = {{QJM}: An International Journal of Medicine},
- author = {Subbe, C.P. and Kruger, M. and Rutherford, P. and Gemmel, L.},
- urldate = {2023-04-30},
- date = {2001-10-01},
- file = {Full Text PDF:/home/ulinja/Zotero/storage/P7TJ5DJB/Subbe et al. - 2001 - Validation of a modified Early Warning Score in me.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/FFJJTX3I/1558977.html:text/html},
-}
-
-@inproceedings{kim_two_2007,
- location = {Berlin, Heidelberg},
- title = {Two Algorithms for Detecting Respiratory Rate from {ECG} Signal},
- isbn = {978-3-540-36841-0},
- doi = {10.1007/978-3-540-36841-0_1030},
- series = {{IFMBE} Proceedings},
- abstract = {Wearable real-time health monitoring technology has been developed for remote diagnosis and health check during daily life. The present study proposes two algorithms to detect respiratory rate from {ECG} signal. One gets respiratory rate by measuring the number of {ECG} samples in R-R interval and its advantage lies in its simplicity. The other detects the rate by measuring the size of R wave in {QRS} signal. This algorithm can detect the rate more robustly but it is complicated and requires the {ECG} signal base line to be stabilized. The preliminary study in laboratory environment showed that the precision of these algorithms was over 97\%.},
- pages = {4069--4071},
- booktitle = {World Congress on Medical Physics and Biomedical Engineering 2006},
- publisher = {Springer},
- author = {Kim, J. M. and Hong, J. H. and Kim, N. J. and Cha, E. J. and Lee, Tae-Soo},
- editor = {Magjarevic, R. and Nagel, J. H.},
- date = {2007},
- langid = {english},
- keywords = {{ECG}, {EDR}, {QRS}, R-R interval},
- file = {Kim et al. - 2007 - Two Algorithms for Detecting Respiratory Rate from.pdf:/home/ulinja/Zotero/storage/YNEGUM7M/Kim et al. - 2007 - Two Algorithms for Detecting Respiratory Rate from.pdf:application/pdf},
-}
-
-@article{smith_ability_2013,
- title = {The ability of the National Early Warning Score ({NEWS}) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death},
- volume = {84},
- issn = {1873-1570},
- doi = {10.1016/j.resuscitation.2012.12.016},
- abstract = {{INTRODUCTION}: Early warning scores ({EWS}) are recommended as part of the early recognition and response to patient deterioration. The Royal College of Physicians recommends the use of a National Early Warning Score ({NEWS}) for the routine clinical assessment of all adult patients.
-{METHODS}: We tested the ability of {NEWS} to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit ({ICU}) admission or death within 24h of a {NEWS} value and compared its performance to that of 33 other {EWSs} currently in use, using the area under the receiver-operating characteristic ({AUROC}) curve and a large vital signs database (n=198,755 observation sets) collected from 35,585 consecutive, completed acute medical admissions.
-{RESULTS}: The {AUROCs} (95\% {CI}) for {NEWS} for cardiac arrest, unanticipated {ICU} admission, death, and any of the outcomes, all within 24h, were 0.722 (0.685-0.759), 0.857 (0.847-0.868), 0.894 (0.887-0.902), and 0.873 (0.866-0.879), respectively. Similarly, the ranges of {AUROCs} (95\% {CI}) for the other 33 {EWSs} were 0.611 (0.568-0.654) to 0.710 (0.675-0.745) (cardiac arrest); 0.570 (0.553-0.568) to 0.827 (0.814-0.840) (unanticipated {ICU} admission); 0.813 (0.802-0.824) to 0.858 (0.849-0.867) (death); and 0.736 (0.727-0.745) to 0.834 (0.826-0.842) (any outcome).
-{CONCLUSIONS}: {NEWS} has a greater ability to discriminate patients at risk of the combined outcome of cardiac arrest, unanticipated {ICU} admission or death within 24h of a {NEWS} value than 33 other {EWSs}.},
- pages = {465--470},
- number = {4},
- journaltitle = {Resuscitation},
- author = {Smith, Gary B. and Prytherch, David R. and Meredith, Paul and Schmidt, Paul E. and Featherstone, Peter I.},
- date = {2013-04},
- pmid = {23295778},
- keywords = {Aged, Early Diagnosis, Female, Heart Arrest, Hospital Mortality, Humans, Intensive Care Units, Male, Patient Admission, Risk Assessment, {ROC} Curve, Severity of Illness Index, United Kingdom, Vital Signs},
- file = {Accepted Version:/home/ulinja/Zotero/storage/WKEEUEAW/Smith et al. - 2013 - The ability of the National Early Warning Score (N.pdf:application/pdf},
-}
-
-@online{noauthor_national_2017,
- title = {National Early Warning Score ({NEWS}) 2},
- url = {https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2},
- abstract = {{NEWS}2 is the latest version of the National Early Warning Score ({NEWS}), first produced in 2012 and updated in December 2017, which advocates a system to standardise the assessment and response to acute illness.},
- titleaddon = {{RCP} London},
- urldate = {2023-05-01},
- date = {2017-12-19},
- file = {Snapshot:/home/ulinja/Zotero/storage/TMN5DTXM/national-early-warning-score-news-2.html:text/html},
+@incollection{hafen_oxygen_2023,
+ location = {Treasure Island ({FL})},
+ title = {Oxygen Saturation},
+ rights = {Copyright © 2023, {StatPearls} Publishing {LLC}.},
+ url = {http://www.ncbi.nlm.nih.gov/books/NBK525974/},
+ abstract = {Oxygen saturation is an essential element in the management and understanding of patient care. Oxygen is tightly regulated within the body because hypoxemia can lead to many acute adverse effects on individual organ systems. These include the brain, heart, and kidneys. Oxygen saturation measures how much hemoglobin is currently bound to oxygen compared to how much hemoglobin remains unbound. At the molecular level, hemoglobin consists of four globular protein subunits. Each subunit is associated with a heme group. Each molecule of hemoglobin subsequently has four heme-binding sites readily available to bind oxygen. Therefore, during the transport of oxygen in the blood, hemoglobin is capable of carrying up to four oxygen molecules. Due to the critical nature of tissue oxygen consumption in the body, it is essential to be able to monitor current oxygen saturation. A pulse oximeter can measure oxygen saturation. It is a noninvasive device placed over a person's finger. It measures light wavelengths to determine the ratio of the current levels of oxygenated hemoglobin to deoxygenated hemoglobin. The use of pulse oximetry has become a standard of care in medicine. It is often regarded as a fifth vital sign. As such, medical practitioners must understand the functions and limitations of pulse oximetry. They should also have a basic knowledge of oxygen saturation.},
+ booktitle = {{StatPearls}},
+ publisher = {{StatPearls} Publishing},
+ author = {Hafen, Brant B. and Sharma, Sandeep},
+ urldate = {2023-08-22},
+ date = {2023},
+ pmid = {30247849},
+ file = {Printable HTML:/home/ulinja/Zotero/storage/ISD8WIRR/NBK525974.html:text/html},
}
diff --git a/docs/figures/components-macro.drawio b/docs/figures/components-macro.drawio
index 3789b1c..d5db8f4 100644
--- a/docs/figures/components-macro.drawio
+++ b/docs/figures/components-macro.drawio
@@ -1 +1,139 @@
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diff --git a/docs/figures/components-macro.png b/docs/figures/components-macro.png
index f60cc9c..e823131 100644
Binary files a/docs/figures/components-macro.png and b/docs/figures/components-macro.png differ
diff --git a/docs/figures/components-micro.drawio b/docs/figures/components-micro.drawio
new file mode 100644
index 0000000..b025fbc
--- /dev/null
+++ b/docs/figures/components-micro.drawio
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/docs/figures/datamodel.drawio b/docs/figures/datamodel.drawio
index 0c5945f..32d7d84 100644
--- a/docs/figures/datamodel.drawio
+++ b/docs/figures/datamodel.drawio
@@ -1 +1,210 @@
-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
\ No newline at end of file
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diff --git a/docs/figures/icon-browser.svg b/docs/figures/icon-browser.svg
new file mode 100644
index 0000000..5cbe1f0
--- /dev/null
+++ b/docs/figures/icon-browser.svg
@@ -0,0 +1,17 @@
+
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+
\ No newline at end of file
diff --git a/docs/figures/icon-server.svg b/docs/figures/icon-server.svg
new file mode 100644
index 0000000..99135d6
--- /dev/null
+++ b/docs/figures/icon-server.svg
@@ -0,0 +1,10 @@
+
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+
\ No newline at end of file
diff --git a/docs/figures/icon-user-interface.svg b/docs/figures/icon-user-interface.svg
new file mode 100644
index 0000000..ffecbbd
--- /dev/null
+++ b/docs/figures/icon-user-interface.svg
@@ -0,0 +1,34 @@
+
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\ No newline at end of file
diff --git a/docs/thesis/cover.tex b/docs/thesis/cover.tex
index b0bc9d1..da62236 100644
--- a/docs/thesis/cover.tex
+++ b/docs/thesis/cover.tex
@@ -1,28 +1,34 @@
\begin{titlepage}
\begin{center}
- {\normalsize\textbf{Early detection of patient deterioration at home using smart medical sensors}} \\
- \vspace{1cm}
+ {\Large\textbf{Early detection of patient deterioration at home using smart medical sensors}} \\
+ \vspace{2cm}
\includegraphics[width=0.5\textwidth]{figures/tubs-logo.png} \\
+ \vspace{2cm}
+ {\large\textbf{Bachelor Thesis}} \\
\vspace{1cm}
- {\large{Bachelor Thesis}} \\
- \vspace{1cm}
- {\small\textbf{
+ {\small{
submitted to \\
- Peter L. Reichertz Institut für Medizinische Informatik \\
- der Technischen Universität Braunschweig \\
- und der Medizinischen Hochschule Hannover \\
+ \vspace{1cm}
+ \textbf{
+ Peter L. Reichertz Institut für Medizinische Informatik \\
+ der Technischen Universität Braunschweig \\
+ und der Medizinischen Hochschule Hannover \\
+ }
\vspace{0.5cm}
- in September 2023 \\
+ in \\
+ \vspace{0.5cm}
+ \textbf{September 2023} \\
\vspace{0.5cm}
by \\
- Julian Lobbes \\
- from Hannover
+ \vspace{0.5cm}
+ \textbf{Julian Lobbes} \\
+ born in Hannover
}} \\
\end{center}
\vfill
{\footnotesize{
- Supervisor: Prof. Dr. Thomas M. Deserno \\
- Supervising assistant: Prof. Dr. Sharareh R. Niakan Kalhori \\
+ Supervisor: \textbf{Prof. Dr. Thomas M. Deserno} \\
+ Supervising assistant: \textbf{Prof. Dr. Sharareh R. Niakan Kalhori} \\
}}
\end{titlepage}
%\newpage
diff --git a/docs/thesis/figures/components-macro.png b/docs/thesis/figures/components-macro.png
new file mode 100644
index 0000000..9ad0dd4
Binary files /dev/null and b/docs/thesis/figures/components-macro.png differ
diff --git a/docs/thesis/figures/datamodel.png b/docs/thesis/figures/datamodel.png
new file mode 100644
index 0000000..462e9d1
Binary files /dev/null and b/docs/thesis/figures/datamodel.png differ
diff --git a/docs/thesis/glossary.tex b/docs/thesis/glossary.tex
index d6d32ae..334373b 100644
--- a/docs/thesis/glossary.tex
+++ b/docs/thesis/glossary.tex
@@ -11,5 +11,26 @@
A Database Management System is a software system which enables the creation, organization, and management of databases.
It generally acts as an interface between the database and client applications, ensuring that data is consistently
stored and readily accessible in a secure and efficient manner, while maintaining data integrity.
+ They can manage various forms of data, including text, numbers, multimedia, and more.
+ The DBMS plays a crucial role in maintaining the integrity, consistency, and security of the data it handles.
+ }
+}
+\newglossaryentry{gui}{
+ type=\acronymtype,
+ name={GUI},
+ description={Graphical User Interface},
+ first={Graphical User Interface (GUI)}
+}
+\newglossaryentry{spo2}{
+ type=\acronymtype,
+ name={SPO\textsubscript{2}},
+ description={\Gls{spo2_full}},
+ first={\Gls{spo2_full} (SPO\textsubscript{2})}
+}
+\newglossaryentry{spo2_full}{
+ name={Blood Oxygen Saturation},
+ description={
+ A percentage measure indicating the level of oxygen saturation in the blood.
+ The blood oxygen saturation represents the proportion of hemoglobin molecules in the bloodstream that are saturated with oxygen\cite{hafen_oxygen_2023}.
}
}
diff --git a/docs/thesis/thesis.tex b/docs/thesis/thesis.tex
index ed4efcd..8d3613a 100644
--- a/docs/thesis/thesis.tex
+++ b/docs/thesis/thesis.tex
@@ -2,6 +2,8 @@
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
+\usepackage{DejaVuSerif} % Serif font
+\usepackage{ascii} % Monospace font
\usepackage{csquotes}
\usepackage[english]{babel}
\usepackage{graphicx}
@@ -55,7 +57,7 @@
commentstyle=\color{codepurple},
keywordstyle=\color{codegray},
stringstyle=\color{codegreen},
- basicstyle=\ttfamily\scriptsize\color{codegray},
+ basicstyle=\asciifamily\color{codegray},
breakatwhitespace=true,
breaklines=true,
captionpos=b,
@@ -67,6 +69,9 @@
}
\lstset{style=mystyle}
+% Inline code snippets
+\newcommand{\code}[1]{\tikz[baseline=(X.base)]\node [draw=gray!50,fill=gray!25,semithick,rectangle,inner sep=2.5pt, rounded corners=3pt] (X) {\asciifamily\color{codegray}{#1}};}
+
% Citations
%\usepackage{cite}
\usepackage[backend=biber, style=vancouver]{biblatex}
@@ -77,26 +82,25 @@
\definecolor{grau}{RGB}{120,110,100}
% A command which generates a TODO message
-\newcommand{\todo}[1]{\fontfamily{lmtt}\selectfont{\color{orange}\small\underline{TODO:}} \textbf{#1}\fontfamily{lmss}\selectfont\normalsize \\}
+\newcommand{\todo}[1]{{\fontfamily{lmtt}\selectfont{\color{orange}\small\underline{TODO:}} \textbf{#1}\normalsize \\}}
\input{./glossary.tex}
\renewcommand{\thepage}{\Roman{page}}
\begin{document}
-{\fontfamily{phv}\selectfont}
+%{\fontfamily{phv}\selectfont}
\pagenumbering{Roman}
\input{cover.tex}
-\section{Summary}
+\section*{Summary}
+\addcontentsline{toc}{section}{Summary}
-\todo{Add: appendix, statement of eigenständigkeit.}
+The summary should be a guideline for creating the work, and briefly name the findings.
-Die Zusammenfassung sollte der Leitfaden der Ausarbeitung sein und einen kurzen Überblick
-über die Ergebnisse geben. Eine maximal einseitige Zusammenfassung ist jeweils in
-deutscher und englischer Sprache anzufertigen.
+A summary must be written in both English and German.
\newpage
\renewcommand*\contentsname{Table of contents}
@@ -107,21 +111,33 @@ deutscher und englischer Sprache anzufertigen.
\pagenumbering{arabic}
\section{Introduction}
-Die Struktur des Einleitungskapitels sollte sich an den einzelnen Stufen des 5-Stufen-Modells1
-zur Vorgehensplanung orientieren. Zunächst beginnen Sie mit dem Abschnitt „Gegenstand
-und Motivation“. In diesem Abschnitt sollen Sie:
+\textbf{Gegenstand und Motivation}
\begin{itemize}
- \item{den Gegenstand Ihrer Arbeit beschreiben, }
- \item{die Bedeutung Ihrer Arbeit für das Umfeld aufzeigen, }
- \item{begründen, welche Problematik zur Erstellung der Arbeit geführt hat, sowie }
- \item{die Motivation für die Erstellung dieser Arbeit erläutern. }
+ \item{brief description of the project}
+ \item{impact on the field}
+ \item{background (what lead to the project?)}
+ \item{motivation, predicted usefulness to the field}
\end{itemize}
-Die weiteren Stufen sind die Beschreibung der Problemstellung, die Zielsetzung der Arbeit
-und die daraus resultierende Fragestellung sowie eine Gliederung ihrer Arbeit.
+\textbf{Problemstellung}
-\todo{write}
+State clearly which problems exist.
+They should phrased such that the project can solve them.
+
+\textbf{Zielsetzung}
+
+State clearly the goals of the research work.
+The goals should be derived clearly from the problem statement, and meeting the goals should solve the stated problems.
+
+\textbf{Frage- und Aufgabenstellung}
+
+List concrete questions/tasks, derived from the goals.
+Completing all tasks should result meeting all goals.
+
+\textbf{Gliederung}
+
+Describe the structure of the work.
\newpage
@@ -131,21 +147,198 @@ und die daraus resultierende Fragestellung sowie eine Gliederung ihrer Arbeit.
\newpage
-\section{Main Section}
+\section{Architecture and Design}
-\todo{write}
+Medwings is designed as a browser-based web application in the classic client-server model, facilitating centralized data storage and evaluation.
+Opting for a web application offers numerous advantages: the primary benefit is its inherent cross-platform compatibility, enabling usage
+on a wide range of devices such as mobile phones and personal computers.
+Secondly, implementing a web application reduces complexity and shortens development time, compared to the creation of a native mobile app coupled
+with a separate, dedicated API server.
+
+\begin{figure}[!ht]
+ \begin{center}
+ \includegraphics[width=.75\textwidth]{./figures/components-macro.png}
+ \caption{\label{fig:components-macro}System diagram showing data flow and user interactions between components in the Medwings environment.}
+ \end{center}
+\end{figure}
+
+The overall system environment is shown in Figure~\ref{fig:components-macro}, depicting the following workflow:
+\begin{enumerate}
+ \item A patient receives a notification on their mobile phone, prompting them to take vitals measurements.
+ \item Upon opening the notification, they are redirected to the Medwings website.
+ Here, they are prompted to self-assess their respiration score by answering a short questionnaire, followed by a prompt to take one measurement
+ on each smart medical device.
+ \item Upon completion of the measurement, each device transmits the data via Bluetooth to the Withings mobile app, installed on the user's phone.
+ The mobile app now sends the data to the Withings Cloud for storage.
+ \item A backend process on the Medwings server awaits the arrival of all recorded measurements from the Withings Cloud, storing them upon reception.
+ Once all required vitals measurements have been retrieved, the MEWS is calculated, stored and displayed to the patient.
+\end{enumerate}
+Measurement prompt notifications are dispatched to the patient at regular intervals throughout the day.
+
+\subsection{Application Modules}\label{sec:modules}
+
+To separate the different functional aspects of Medwings according to responsibility, its application code is split into the following five modules:
+\begin{itemize}
+ \item \code{core}
+ \item \code{withings}
+ \item \code{gotify}
+ \item \code{authentication}
+ \item \code{medwings}
+\end{itemize}
+Each module defines classes representing backend logic, database schemas and user interface elements pertaining to its specific function.
+Implementation details are encapsulated within these classes, while public interfaces are exposed to external program code to provide each module's core functionality.
+
+The \code{core} module forms the backbone of the application.
+It encompasses configuration settings, secrets such as private encryption keys or API tokens, and functionalities shared across multiple other modules.
+
+Medwings interfaces with the Withings Cloud through the \code{withings} module.
+This includes retrieving vitals data through authenticated requests to the Withings Cloud API, which implements the OAuth 2.0 Authorization Framework.
+As per its specification, \enquote{In OAuth, the client requests access to resources controlled by the resource owner and hosted by the resource server\ldots~
+ Instead of using the resource owner’s credentials to access protected resources, the client obtains an access token\ldots~
+ The client uses the access token to access the protected resources hosted by the resource server.
+}\cite{hardt_oauth_2012}
+While this process is largely transparent for the resource owner --- the patient in this case --- the communication between
+Medwings as the resource client and Withings as the resource server is complex, and is therefore abstracted by the module.
+Aside from OAuth 2.0, \code{withings} also encapsulates fetching, parsing, and storing vitals data retrieved from Withings.
+
+Medwings implements a standalone user authentication system, which is provided by the \code{authentication} module.
+Patients must register with a username and password to be able to use the application.
+The registration occurs in three stages:
+\begin{enumerate}
+ \item The patient grants Medwings the permission to retrieve their health data from Withings in an OAuth2 authorization flow.
+ \item A registration form is shown, prompting the user to choose a username and password, and to enter relevant personal information.
+ \item The user is shown a confirmation that the account was created successfully, and is asked to download the Gotify app, described below, and log in using their Medwings credentials.
+\end{enumerate}
+Following registration, the supplied information and numerous authentication tokens are saved in the Medwings database.
+Patients can now sign in on the Medwings website.
+
+The \code{medwings} module, pivotal to the core functionalty of Medwings, defines the data model used to represent and store the various vital signs handled by the application.
+Furthermore, it provides interfaces to access the data, as well as the algorithm used to calculate the MEWS.
+
+In order to send push notifications to mobile devices, Medwings leverages \textit{Gotify} -- a dedicated notification microservice\cite{noauthor_gotify_nodate}.
+Gotify is composed of a web server component, and a mobile app acting as the client software.
+The server exposes its own API, which allows external applications like Medwings to dispatch push notifications programmatically.
+It uses an independent database for client authentication. The \code{gotify} module ensures synchronization between the user databases of Gotify and Medwings.
+In addition, the module provides interfaces to send customized push notifications to specific patients.
+
+\subsection{Data Model}
+
+A relational database is used to store application data, whereby each Medwings module defines the database schema for the underlying data it is responsible for handling.
+Module interdependencies correlate closely with the foreign key references in the data model.
+A holistic representation of the Medwings data model is shown in Figure~\ref{fig:datamodel}.
+
+\begin{figure}[!ht]
+ \begin{center}
+ \includegraphics[width=\textwidth]{./figures/datamodel.png}
+ \caption{\label{fig:datamodel}Entity-Relationship diagram (Crow's Foot notation) showing the data model of the Medwings database.}
+ \end{center}
+\end{figure}
+
+At its heart lies the \code{User} entity: it forms the nexus to which all vitals data and user information is linked.
+Withings API tokens are stored in the \code{RefreshToken} and \code{AccessToken} entities, while the \code{GotifyUser} and \code{GotifyAccount} entities retain the Gotify API credentials.
+The numerous vital signs, as well as the MEWS record which can potentially be calculated based on them, are also represented.
+The \code{Profile} table stores additional medically relevant patient information as supplied during user registration.
+
+\subsection{Deployment}
+
+To use the smart devices to take measurements, patient users must first install the Withings mobile app on their phone, and use it to create a Withings user account.
+Following registration, each device must be linked to the app and configured via Bluetooth.
+Some basic configuration is required in order to enable specific device features, such as measurement of \Gls{spo2} on the Scanwatch.
+Users are guided through the process by the app's \Gls{gui}.
+
+Being a web application, no installation is necessary to access the Medwings interface, patients simply visit the website in a web browser.
+Patients do need to create a Medwings account on the website however, followed by installation and configuration of the Gotify mobile app, as described in the registration
+process in Section~\ref{sec:modules}.
+
+The centralized server components, including the Gotify server, a task scheduler used to schedule sending notifications and the Medwings application code itself are deployed
+on a publicly accessible web server using a Docker container environment.
+
+\subsection{Design Challenges}
+
+Since managing a user in Medwings requires the respective user's state to be mirrored by two other services, Withings and Gotify, keeping user accounts across
+all three services in sync presents a challenge.
+Particularly during user creation, user accounts must be linked to Withings, created on the Gotify server and finally saved to the Medwings database.
+Various integrity checks, such as when the user aborts the registration process midway, were put in place to prevent diverging user states across the three services
+and overcome this challenge.
+
+Similarly, vitals records kept in the Medwings database must be synchronized with all records available on the Withings cloud.
+Regularly recurring, as well as on-demand data synchronization hooks were implemented to keep the Medwings database up to date,
+while database constraints ensure validity of imported data and prevent duplication of existing records.
+
+The non-enterprise Withings API enforces a rate limit of 120 requests per minute.
+Medwings polls the API regularly to retrieve the latest health data for patients.
+At scale, with many patient users, the rate limit would quickly be reached.
+The Withing API does provide functionality to notify client applications upon availability of new data, making it possible to avoid polling.
+Given that Medwings was only used by a single patient user during the trial phase, falling back to polling was an acceptable compromise to lower complexity
+while still operating within the rate limit.
+
+A MEWS calculation should represent the patient's overall physiological state at -- ideally -- a discrete point in time.
+Naturally, there is a delay from when a measurement is taken with a device until it can be retrieved from the API.
+The percieved transmission delay in the Medwings implementation was generally consistent with what is stated in the Withings public API documentation:
+\enquote{Delays are typically less than two minutes, but it can be longer.}\cite{noauthor_keep_nodate}.
+However, in some cases, the measurements taken on a device do not get pushed to the Withings Cloud until much later, or fail to do so at all.
+While the cause for these longer than normal delays and missing data points is unknown and outside of the control of Medwings, these edge cases
+had to be taken into account.
+Furthermore, the time it takes a patient to take measurements using all three devices must also be accounted for.
+Therefore, Medwings enforces a maximum allowed time difference of ten minutes between measurements of the different vitals records used to calculate MEWS.
+If a set of vitals measurements is, across all records in the set, spaced further apart than ten minutes, no MEWS record is calculated, and the user is shown an
+error message, prompting them to repeat the measurements.
\newpage
-\section{Implementation}
-
-\todo{write}
+\section{System Interaction and Usability}
+\begin{itemize}
+ \item Personal experiences in interacting with the system and the medical devices
+ \item What went well and what didn't? Why?
+ \item Usability for potential medical staff, if applicable
+ \item Strengths and weaknesses of patients taking their own measurements vs. having a medical professional take them
+ \item How well did patients (or you, in this case) adhere to the measurement schedule? What factors influenced this?
+\end{itemize}
\newpage
-\section{Evaluation}
+\section{Data Presentation and Analysis}
+\begin{itemize}
+ \item Present your measured vitals data in an organized and visual manner (tables, graphs, etc.)
+ \item Analyze the vitals and MEWS data: trends, anomalies, etc.
+ \item Discuss the regularity of measurements and the system’s effectiveness in prompting and collecting data
+\end{itemize}
-\todo{write}
+
+\newpage
+\section{Evaluation and Validation}
+\begin{itemize}
+ \item Describe the methods used to evaluate the system
+ \item Discuss the validity of the study, especially considering that the test patient was yourself
+ Explicitly state the limitations of your study. For example, the fact that you were the test patient, which might introduce bias into the results
+ \item Are the results likely to generalize? Why or why not?
+\end{itemize}
+
+
+\newpage
+\section{Lessons Learned and Reflections}
+\begin{itemize}
+ \item Reflect on the overall development process
+ \item What would you do differently if you were to start over?
+ \item What did you learn that was unexpected?
+\end{itemize}
+
+
+\newpage
+\section{Future Work and Improvements}
+\begin{itemize}
+ \item What can be improved in the current system?
+ \item Are there additional features that could make the system more effective?
+ \item Any scalability or security issues that would need to be addressed for a larger-scale deployment?
+\end{itemize}
+
+\newpage
+\section{Implications and Conclusions}
+\begin{itemize}
+ \item Implications of this research for the wider field of remote patient monitoring
+ \item Conclusions drawn from the research
+ \item Summary of the contributions of your work
+\end{itemize}
\newpage
@@ -153,6 +346,7 @@ und die daraus resultierende Fragestellung sowie eine Gliederung ihrer Arbeit.
\setcounter{page}{3}
\pagenumbering{Roman}
\printnoidxglossary[title=Glossary, toctitle=Glossary]\label{sec:glossary}
+
\newpage
\printnoidxglossary[type=\acronymtype, title=Acronyms, toctitle=Acronyms]\label{sec:acronyms}
@@ -167,8 +361,19 @@ und die daraus resultierende Fragestellung sowie eine Gliederung ihrer Arbeit.
% Appendix here
-\section{Ehrenwörtliche Erklärung}
-% Eigenständigkeitserklärung here
-\todo{write}
+\newpage
+\section*{Ehrenwörtliche Erklärung}
+\addcontentsline{toc}{section}{Ehrenwörtliche Erklärung}
+
+Ich versichere, dass ich die beiliegende Bachelorarbeit ohne Hilfe Dritter und ohne Benutzung anderer als der angegebenen
+Quellen und Hilfsmittel angefertigt und die den benutzten Quellen wörtlich oder inhaltlich entnommenen Stellen als solche
+kenntlich gemacht habe.
+Diese Arbeit hat in gleicher Form noch keiner Prüfungsbehörde vorgelegen.
+
+Ich bin mir bewusst, dass eine falsche Erklärung rechtliche Folgen haben wird.
+
+
+Braunschweig, 12.09.2023
+\vspace{3cm}
\end{document}