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Interactive Predictive Analytics for Enhancing Patient Adherence in Remote Health Monitoring

Although Remote Health Monitoring (RHM) systems have shown potential for improving quality of care and reducing healthcare costs, low adherence of human subjects can dramatically degrade the system efficacy. The purpose of this research is to design and develop an interactive and human-centered framework with new data-driven techniques and predictive analytics algorithms to enhance patients' engagement and compliance with RHM systems. In this paper, we propose a novel interactive data-driven system for on-demand data acquisition to enhance human subjects' adherence in a RHM system. In this approach, we develop a predictive analytics model that attempts to predict medical conditions with the least amount of collected data in order to reduce patients' burden and improve the adherence. The proposed framework includes a data-driven unit named Interactive Learning for Data Acquisition (ILDA) for on-demand data collection in order to enhance the prediction confidence and accuracy as needed. The ILDA automatically decides whether it still needs to acquire additional information from some specific subjects or not. It is responsible for evaluating the confidence of the predictions, detecting the need to interact with specific subjects to collect new data, and identifying what additional information should be requested from which subjects. The proposed method has been tested and validated on two different datasets from diabetic and heart disease patients.