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.
@article{arxiv.1609.04466,
title = {Phenotyping using Structured Collective Matrix Factorization of Multi--source EHR Data},
author = {Suriya Gunasekar and Joyce C. Ho and Joydeep Ghosh and Stephanie Kreml and Abel N Kho and Joshua C Denny and Bradley A Malin and Jimeng Sun},
journal= {arXiv preprint arXiv:1609.04466},
year = {2016}
}