Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.

Journal: Anesthesiology
Published Date:

Abstract

BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge.

Authors

  • Velibor V Mišić
    From Decisions, Operations, and Technology Management Area, Anderson School of Management (V.V.M., K.R.) Department of Anesthesiology and Perioperative Medicine (E.G., I.H.), University of California Los Angeles, Los Angeles, California Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania (A.M.).
  • Eilon Gabel
  • Ira Hofer
  • Kumar Rajaram
  • Aman Mahajan
    Department of Anaesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.