Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators.

Journal: Journal of the American College of Cardiology
PMID: 39570241

Abstract

BACKGROUND: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional statistical methods and provide more accurate, personalized risk estimates.

Authors

  • Lindsey Rosman
    Department of Medicine, Division of Cardiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. Electronic address: Lindsey_Rosman@med.unc.edu.
  • Rachel Lampert
    Department of Internal Medicine (Cardiovascular Medicine), Yale School of Medicine, New Haven, Connecticut, USA.
  • Kaicheng Wang
    Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.
  • Anil K Gehi
    Department of Medicine, Division of Cardiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • James Dziura
    Yale Center for Analytic Sciences, Yale School of Public Health, New Haven, Connecticut, USA; Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, Rhode Island, USA.
  • Elena Salmoirago-Blotcher
    Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, Rhode Island, USA; Schools of Medicine and Public Health, Brown University, Providence, Rhode Island, USA.
  • Cynthia Brandt
    Yale Center for Medical Informatics, Yale University.
  • Samuel F Sears
    Department of Psychology, East Carolina University, Greenville, North Carolina, USA; Department of Cardiovascular Sciences, East Carolina Heart Institute, East Carolina University, Greenville, North Carolina, USA.
  • Matthew Burg
    Department of Internal Medicine (Cardiovascular Medicine), Yale School of Medicine, New Haven, Connecticut, USA; VA Connecticut Healthcare System, West Haven, Connecticut, USA; Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut, USA.

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