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README.md
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This repository contains the software, research data and final thesis I worked on
as part of my Computer Science degree at TU Braunschweig.
My research examined the current state of research in mobile patient deterioration monitoring,
and introduced a novel software system, which acts as an early warning system for high-risk patients
Over the course of three months, my research examined the current state of research in mobile patient deterioration monitoring,
and introduced and evaluated a novel software system, which acts as an early warning system for high-risk patients
outside of direct medical supervision by utilising smart medical sensors in combination with
automated early warning score calculations.
Following the conception and development of the software, the system underwent a usability trial
@ -26,7 +26,7 @@ To calculate an early warning score, or MEWS specifically, several vital paramet
scored individually. The individual scores are then added together to produce the final MEWS, which gives a clear
indication of the patient's risk of deterioration. The following table shows the vital parameters and scoring for MEWS:
![MEWS Scoring Table](/docs/figures/mews-calculation-table.png)
![MEWS Scoring Table](./docs/figures/mews-calculation-table.png)
Using tradtitional, expensive and immobile vitals monitoring devices, early warning scores are well established and widely used.
But what about at home or on the go? With recent advances in smart medical sensor technologies, predicting patient
@ -46,11 +46,11 @@ patients are asked to visit the web-UI, followed by recording their vital parame
These devices are shown the following picture:
![Withings Smart Medical Devices](/docs/figures/withings-devices.png)
![Withings Smart Medical Devices](./docs/figures/withings-devices.png)
Some UI screenshots of this process are shown here:
![Medwings Measurement Screenshots](/docs/figures/medwings-measurement-screenshots.png)
![Medwings Measurement Screenshots](./docs/figures/medwings-measurement-screenshots.png)
Following the successful measurement using all three devices, Medwings retrieves the recorded vitals data from the Withings API,
processes it, calculating the patient's MEWS, and stores the results. The patient user is given feedback through the UI and, should
@ -63,20 +63,20 @@ notifications. It communicates with the Withings Health Data Cloud via the RESTf
requests and to link Withings user accounts to Medwings. The dedicated Gotify Server used by Medwings is also controlled via
its own REST API.
The Medwings application is split itself into the following modules, each handling a specific responsibility:
The Medwings application itself is split into the following modules, each handling a specific responsibility:
- [core](/app/core/): globally shared files and application-wide configuration
- [authentication](/app/authentication/): Medwings user and login/logout management
- [medwings](/app/medwings/): everything related to vitals data processing, storage and MEWS calculation
- [gotify](/app/gotify/): interfaces to the notification server
- [withings](/app/withings/): interfaces to the Withings API
- [core](./app/core/): globally shared files and application-wide configuration
- [authentication](./app/authentication/): Medwings user and login/logout management
- [medwings](./app/medwings/): everything related to vitals data processing, storage and MEWS calculation
- [gotify](./app/gotify/): interfaces to the notification server
- [withings](./app/withings/): interfaces to the Withings API
You can read more about each module and its functionality in each section mentioned above.
#### Further Reading
For more details about Medwings, its architecture, the development process and the in-depth evaluation and usability
study of the system, you can read the [complete paper here](/docs/thesis/thesis.pdf).
study of the system, you can read the [complete paper here](./docs/thesis/thesis.pdf).
# Development Guide