Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.

Journal: JAMA network open
Published Date: Jan 3, 2020

Abstract

IMPORTANCE: Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients' quality of life and outcomes.

Authors

  • Rishi J Desai
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Shirley V Wang
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont St Suite 303, Boston, MA, 02120, USA. swang1@bwh.harvard.edu.
  • Muthiah Vaduganathan
    Brigham and Women's Hospital Heart and Vascular Center, Department of Medicine, Harvard Medical School, Boston, MA.
  • Thomas Evers
    Market Access, Bayer AG, Wuppertal, Germany.
  • Sebastian Schneeweiss
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.

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