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Activity Number: 43 - Discovering Homology in Multi-View Data: New Statistical Methods for Data Integration
Type: Invited
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #326921
Title: Joint Modeling of Multi-System Wearable Data
Author(s): Vadim Zipunnikov* and Junrui Di
Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
Keywords: sleep; physical activity; circadian rhythmicity; matrix decomposition; tensor decompositions

Physical activity and sleep trackers as well as heart rate monitors are now extensively used to track quality of sleep, levels of physical activity, and disruptions in circadian rhythms in many clinical studies, for example, to reliably monitor and assess post-intervention changes in patient's status. Thus, mobile technologies has provided an unprecedented opportunity to obtain objective simultaneous assessment of multiple physiological systems in real-time, over weeks or months. We developed a computationally efficient data analytical frameworkte that parsimoniously models multiple biomarkers nested within multiple systems, accounts for interactions of biomarkers within and between physiological systems, and decomposes within-subject and between-subject variability. We illustrate our approach using 7-day of Actiheart data from 106 participants of Baltimore Longitudinal Study of Aging.

Authors who are presenting talks have a * after their name.

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