Quantitative Issues in Cancer Research Working Seminar

Patrick Emedom-Nnamdi
Doctoral Student, Department of Biostatistics, Harvard University

“Hidden Markov Models and Considerations for Digital Phenotyping Data”

ABSTRACT: Recent developments in collecting individual-level phenotypic data in free-living settings through wearable devices and smartphones have afforded researchers the ability to sample high fidelity data that concern human behavior and health. Such advancements are paving the way for researchers interested in studying social, behavioral, and cognitive phenotypes that have proven to have a temporal and contextual dependence. While digital phenotyping provides a robust and efficient mechanism for collecting temporally dense data on populations of interest, challenges remain in developing tools that utilize this data for identifying dynamic behavioral trends among heterogeneous subjects. In this talk, I will explore approaches towards advancing classical hidden Markov models to address these challenges (i.e. high-dimensionality, state representation, heterogeneity, temporal resolution) in effort to reveal clinically meaningful modes of differentiation in subject behavior.