Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name "deep patient". We evaluated this…

• NI
  National Institutes of Health

  Awards: R01DK098242, UL1TR001433, U54CA189201
• NC
  National Cancer Institute

  Award: U54CA189201
• NI
  National Institute of Diabetes and Digestive and Kidney Diseases

  Award: R01DK098242
• NC
  National Center for Advancing Translational Sciences

  Award: UL1TR001433