QuBBD: From Personalized Predictions to Better Control of Chronic Health Conditions

Sponsor: National Science Foundation

Award Number: DMS-1664644

PI: Ioannis (Yannis) Ch. Paschalidis

Co-Is/Co-PIs: Christos Cassandras, Rebecca Mishuris

Th

The research will focus on two broad tasks: (1) predictive analytics, and (2) personalized interventions. Task 1 develops methods for predictions in two time scales, long and medium. These predictions target hospitalizations and rely upon new supervised machine learning approaches that combine classification with clustering as a way of enhancing performance and offering interpretable results. In addition, anomaly detection methods are proposed for shorter-term predictions. Task 2 focuses on interventions seeking to prevent events predicted under Task 1. Interventions include increased monitoring and optimizing treatment policies using Markov Decision Processes and perturbation analysis methods. Methodological advances will include methods for joint clustering and classification, anomaly detection, learning and improving policies for Markov Decision Processes, and perturbation analysis techniques.

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