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Mobile Apps and Machine Learning for Improving Healthcare

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SINW01 - Scalable statistical inference

The first part of this talk centers on the analysis of student influenza data. Students in dormitories at the University of Michigan were given smartphones with mobile a mobile app, called iEpi, that captured data about their locations, interactions, and disease symptoms. We develop Graph-coupled Hidden Markov Models (GCHMMs) which use this data to predict whether a student was likely to fall ill due to their interactions. Using a hierarchical version of GCHM Ms we can combine with demographic data and see that certain characteristics, such as drinking, and poor sleep quality, increased the likelihood of contracting influenza, as well as recovery time.

The second part discusses the development of a new mobile app, MS Mosaic, for tracking symptoms in multiple sclerosis (MS) patients. The app includes data in the form of daily surveys, fitness tracker information, and mobile phone task data. The daily surveys about symptoms and medications can potentially be completed with a single notification swipe, sleep and activity data can be collected passively using HealthKit, and mobile phone tasks include finger tapping, gait analysis, as well as additional cognitive and motor tasks. Data collected provides an opportunity for the development of novel machine learning methods for learning about chronic disease, and novel sensor types. The app will soon be released to the Apple app store, and piloted in clinic at Duke University.

If time remains we will briefly look at some of the other healthcare work on using Gaussian Process models on EHR data, going on currently at Duke.

Coauthors: Kai Fan, Allison Aiello, Lee Hartsell, Joe Futoma, and Sanjay Hariharan

This talk is part of the Isaac Newton Institute Seminar Series series.

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