Computational phenotyping via tensor factorization

Our research addresses the problem of transforming raw electronic health record (EHR) data to medical concepts with minimal human intervention. We posit the use of multi-relational tensor factorization approaches to generate concise and clinically relevant phenotypes. The tensor framework provides powerful, data-driven, and interpretable approaches for transforming high-dimensional EHR data into medical concepts.

This is a collaboration with UT-Austin, Georgia Tech, Vanderbilt, and Northwestern.

Phenotyping via nonnegative tensor factorization