Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer.

Journal: JAMA oncology
PMID: 32970131

Abstract

IMPORTANCE: Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices.

Authors

  • Christopher R Manz
    Harvard Medical School, Boston, MA.
  • Jinbo Chen
    Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Manqing Liu
    Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
  • Corey Chivers
    Penn Medicine, University of Pennsylvania, Philadelphia.
  • Susan Harkness Regli
    Penn Medicine, University of Pennsylvania, Philadelphia.
  • Jennifer Braun
    Abramson Cancer Center, University of Pennsylvania, Philadelphia.
  • Michael Draugelis
    University of Pennsylvania Health System, Philadelphia, PA.
  • C William Hanson
    University of Pennsylvania Health System, Philadelphia, PA.
  • Lawrence N Shulman
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
  • Lynn M Schuchter
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
  • Nina O'Connor
    University of Pennsylvania, Philadelphia, PA, United States of America.
  • Justin E Bekelman
    University of Pennsylvania, Philadelphia, PA, United States of America; Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, United States of America.
  • Mitesh S Patel
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
  • Ravi B Parikh
    Division of Hematology and Oncology, Perelman School of Medicine, University of Philadelphia, Philadelphia, Pennsylvania.

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