Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

Journal: Critical care medicine
Published Date: Jul 1, 2018

Abstract

OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, random forest, and elastic net regression models, with and without unstructured clinical text data, to predict a binary composite outcome of in-hospital death or ICU length of stay greater than or equal to 7 days using data from the first 48 hours of hospitalization.

Authors

  • Gary E Weissman
    1 Pulmonary, Allergy, and Critical Care Division, Hospital of the University of Pennsylvania.
  • Rebecca A Hubbard
    Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Lyle H Ungar
    Department of Computer & Information Science, University of Pennsylvania.
  • Michael O Harhay
    2 Leonard Davis Institute of Health Economics, and.
  • Casey S Greene
    Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, United States; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania, United States. Electronic address: csgreene@upenn.edu.
  • Blanca E Himes
    Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Scott D Halpern
    1 Pulmonary, Allergy, and Critical Care Division, Hospital of the University of Pennsylvania.

Keywords

External Resources

Popular Topics
Recent Journals