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The increased availability of electronic health records (EHRs) havespearheaded the initiative for precision medicine using data driven approaches.Essential to this effort is the ability to identify patients with certainmedical conditions of interest from simple queries on EHRs, or EHR-basedphenotypes. Existing rule--based phenotyping approaches are extremely laborintensive. Instead, dimensionality reduction and latent factor estimationtechniques from machine learning can be adapted for phenotype extraction withno (or minimal) human supervision.  We propose to identify an easily interpretable latent space shared acrossvarious sources of EHR data as potential candidates for phenotypes. Byincorporating multiple EHR data sources (e.g., diagnosis, medications, and labreports) available in heterogeneous datatypes in a generalized\textit{Collective Matrix Factorization (CMF)}, our methods can generate richphenotypes. Further, easy interpretability in phenotyping application requiressparse representations of the candidate phenotypes, for example each phenotypederived from patients' medication and diagnosis data should preferably berepresented by handful of diagnosis and medications, ($5$--$10$ activecomponents). We propose a constrained formulation of CMF for estimating sparsephenotypes. We demonstrate the efficacy of our model through an extensiveempirical study on EHR data from Vanderbilt University Medical Center.

Phenotyping using Structured Collective Matrix Factorization of Multi--source EHR Data