Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

Journal: PLoS medicine
Published Date:

Abstract

BACKGROUND: Pythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an effort to better identify high-risk surgical patients from complex data, a machine learning project trained on Pythia was built to predict postoperative complication risk.

Authors

  • Kristin M Corey
    Duke Institute for Health Innovation, Durham, North Carolina, United States of America.
  • Sehj Kashyap
    Duke Institute for Health Innovation, Durham, North Carolina, United States of America.
  • Elizabeth Lorenzi
    Department of Statistical Sciences, Duke University, Durham, North Carolina, United States of America.
  • Sandhya A Lagoo-Deenadayalan
    Department of Surgery, Duke University, Durham, North Carolina, United States of America.
  • Katherine Heller
    Department of Statistical Sciences.
  • Krista Whalen
    Duke Institute for Health Innovation, Durham, North Carolina, United States of America.
  • Suresh Balu
    Duke Institute for Health Innovation.
  • Mitchell T Heflin
    Division of Geriatrics, Department of Medicine, Duke University, Durham, North Carolina, United States of America.
  • Shelley R McDonald
    Division of Geriatrics, Department of Medicine, Duke University, Durham, North Carolina, United States of America.
  • Madhav Swaminathan
    Department of Anesthesiology, Duke University, Durham, North Carolina, United States of America.
  • Mark Sendak
    Duke Institute for Health Innovation.