  The increased availability of electronic health records (EHRs) have
spearheaded the initiative for precision medicine using data driven approaches.
Essential to this effort is the ability to identify patients with certain
medical conditions of interest from simple queries on EHRs, or EHR-based
phenotypes. Existing rule--based phenotyping approaches are extremely labor
intensive. Instead, dimensionality reduction and latent factor estimation
techniques from machine learning can be adapted for phenotype extraction with
no (or minimal) human supervision.
  We propose to identify an easily interpretable latent space shared across
various sources of EHR data as potential candidates for phenotypes. By
incorporating multiple EHR data sources (e.g., diagnosis, medications, and lab
reports) available in heterogeneous datatypes in a generalized
\textit{Collective Matrix Factorization (CMF)}, our methods can generate rich
phenotypes. Further, easy interpretability in phenotyping application requires
sparse representations of the candidate phenotypes, for example each phenotype
derived from patients' medication and diagnosis data should preferably be
represented by handful of diagnosis and medications, ($5$--$10$ active
components). We propose a constrained formulation of CMF for estimating sparse
phenotypes. We demonstrate the efficacy of our model through an extensive
empirical study on EHR data from Vanderbilt University Medical Center.
