Learning Interpretable, Temporal Health Status Phenotypes from Self-Tracked Patient Data

Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Personal Health Informatics, Patient / Person Generated Health Data (Patient Reported Outcomes), Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Endometriosis is a debilitating, systemic chronic illness where unpredictable week-to-week variations care. We hypothesize that unsupervised probabilistic phenotype approaches can enable meaningful, interpretable representations of health status over time in the context of self-tracked data, independently of an individual’s level of engagement with self-tracking. We generate and evaluate temporal phenotypes from self-tracking data to represent individuals’ illness states over time, which have the potential to support new tools for tracking and management.

Speaker(s):
Adrienne Pichon
Columbia University, Department of Biomedical Informatics

Author(s):
Adrienne Pichon - Columbia University, Department of Biomedical Informatics; Noemie Elhadad, PhD - Columbia University;