An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data

Abstract

Mobile sensing and analysis of data streams collected from personal devices such as smartphones and fitness trackers have become useful tools to help health professionals monitor and treat patients outside of clinics. Research in mobile health has largely focused on feasibility studies to detect or predict a health status. Despite the development of tools for collection and processing of mobile data streams, such approaches remain ad hoc and offline. This paper presents an automated machine learning pipeline for continuous collection, processing, and analysis of mobile health data. We test this pipeline in an application for monitoring and predicting adolescents’ mental health. The paper presents system engineering considerations based on an exploratory machine learning analysis followed by the pipeline implementation.