Investigators – Deborah Estrin, Tanzeem Choudhury, Geri Gay, JP Pollak, Phil Adams
Patient-Centric Disease Management Using Automatically Inferred Behavioral Biomarkers and Sensor-Supported Contextual Self-Report: the vision of patient-centric, personalized, precision medicine and wellness will be fully realized only when an individual’s self-care and clinical decision making are informed by a rich, predictive model of that individual’s health status. This project addresses the challenge of transforming these behavioral data streams into robust measures relevant to individual health and clinical decisionmaking.
The key contributions of this work include development and evaluation of:
1) software techniques to combine and transform passively monitored and self-reported data streams into clinically meaningful, actionable, and personalized indicators, which we call behavioral biomarkers;
2) contextual recall that allows the collection of highly granular and contextually specific self-report data to enhance passively captured data with information from the patient perspective, while balancing the tension faced in balancing recall bias and usability;
and 3) a methodology that systematizes the collaboration with clinical domain experts.