Chronic Illness Tracker App

There are many poorly understood medical conditions where symptoms vary across time yet sufferers cannot pinpoint why some days are better than others (e.g. irritable bowel, ongoing fatigue, and complex interacting conditions). Our hypothesis is that machine learning applied to individual time-series data can identify factors affecting symptoms. To test this we collect data from three sources (Fitbit, a nutritional app, and a symptom/medication tracker) and apply machine learning to identify causal hypotheses.

This project is to develop the symptom tracker component as a native mobile app (Android and iOS) for participants to use to track their symptoms and medications/supplements. To be as compatible as possible with the analysis pipeline, React Native with an AWS backend is proposed.

Successful completion of the project will result in the project being used for the trial study (and startup product if successful).

Client


Contact: Dr Chris Bartley
Phone: 0419 964 128
Email[email protected]
Preferred contact: Phone,Email
Location: Perth

IP Exploitation Model


The IP exploitation model requested by the Client is: IP to be assigned to the project proposer(s)



Department of Computer Science & Software Engineering
The University of Western Australia
Last modified: 23 July 2019
Modified By: Michael Wise
UWA