Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

Journal: JAMA network open
PMID: 34019087

Abstract

IMPORTANCE: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior predictive models to aid in clinical decision-making.

Authors

  • Jeph Herrin
    Division of Cardiology, Yale School of Medicine, New Haven, Connecticut.
  • Neena S Abraham
    Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Scottsdale, Arizona.
  • Xiaoxi Yao
    Department of Health Sciences Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Jonathan Inselman
    Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota.
  • Nilay D Shah
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
  • Che Ngufor
    Mayo Clinic, Rochester, MN.

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