Models for clinical risk prediction are important tools for clinicians to understand the risks of adverse event decision-making, stratify patient populations, and enable shared decision-making on treatments. However, conventional models represent patient risk using fixed time and space for inputs and outputs, and following these patients out the door with remote sensing has remained limited in success. Significant gaps exist in developing adaptable models that represent patient status and risk across the vast heterogeneity in available data, and in updating patient representations across the continual collection of new data. This is particularly true when incorporating the growing breadth of wearable sensing health data where obtaining ground truth labeling associating wearable signals with clinical data and outcomes is particularly challenging. This talk will cover methods designed to bridge the gap, following the course of a hospitalization and recovery typically found in patients with diagnosed heart failure. The talk will first cover the need for time-varying, multimodal models of care with clinical data, and then discuss the challenges in both design and modeling of wearable sensing data intended to help track trajectories of recovery after discharge.
Bobak Mortazavi, PhD, is an Associate Professor of Computer Science & Engineering at Texas A&M University and holds an affiliation with the Yale University School of Medicine’s Center for Outcomes Research and Evaluation. His research focuses on the intersection of wearable technology, machine learning, and cardiovascular-focused clinical outcomes research, to develop longitudinal, personalized models of health. As a member of the PATHS-UP Engineering Research Center, he has made important contributions in enabling wearable sensing technologies for personal health monitoring and integrating machine learning modeling for improving the use of this data in the context of clinical outcomes.