Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.

Journal: Journal of medical Internet research
PMID: 32202503

Abstract

BACKGROUND: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications).

Authors

  • Subendhu Rongali
    College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States.
  • Adam J Rose
    Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, United States.
  • David D McManus
    Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.
  • Adarsha S Bajracharya
    Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.
  • Alok Kapoor
    Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.
  • Edgard Granillo
    Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States.
  • Hong Yu
    University of Massachusetts Medical School, Worcester, MA.

Keywords

External Resources

Popular Topics
Recent Journals