Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.

Journal: Annals of emergency medicine
Published Date: Dec 24, 2020

Abstract

STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-learning models.

Authors

  • Dana R Sax
    Department of Emergency Medicine, The Permanente Medical Group, Oakland, CA. Electronic address: dana.r.sax@kp.org.
  • Dustin G Mark
    Department of Emergency Medicine, The Permanente Medical Group, Oakland, CA.
  • Jie Huang
    Department of Critical Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Oleg Sofrygin
    Division of Research, Kaiser Permanente Northern California, Oakland, CA.
  • Jamal S Rana
    Division of Cardiology, The Permanente Medical Group, Kaiser Permanente, Oakland, CA 94611, USA.
  • Sean P Collins
    Department of Chemistry and Biomolecular Science, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada.
  • Alan B Storrow
    Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN.
  • Dandan Liu
    Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN.
  • Mary E Reed
    Division of Research, Kaiser Permanente Northern California, Oakland, CA.

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