Cardiovascular and Metabolic Risk

Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score

  1. Matthew W. Segar1,
  2. Muthiah Vaduganathan2,
  3. Kershaw V. Patel1,
  4. Darren K. McGuire1,
  5. Javed Butler3,
  6. Gregg C. Fonarow4,
  7. Mujeeb Basit1,
  8. Vaishnavi Kannan5,
  9. Justin L. Grodin1,
  10. Brendan Everett2,
  11. Duwayne Willett1,
  12. Jarett Berry1 and
  13. Ambarish Pandey1⇑
  1. 1Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
  2. 2Brigham and Women’s Hospital Heart and Vascular Center, Department of Medicine, Harvard Medical School, Boston, MA
  3. 3Department of Medicine, University of Mississippi Medical Center, Jackson, MS
  4. 4Division of Cardiology, Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan UCLA Medical Center, Los Angeles, CA
  5. 5Department of Health System Information Resources (Clinical Informatics), University of Texas Southwestern Medical Center, Dallas, TX
  1. Corresponding author: Ambarish Pandey, ambarish.pandey{at}utsouthwestern.edu

  1. M.W.S. and M.V. contributed equally as co–first authors of this manuscript.

Diabetes Care 2019 Dec; 42(12): 2298-2306. https://doi.org/10.2337/dc19-0587

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Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score
Matthew W. Segar, Muthiah Vaduganathan, Kershaw V. Patel, Darren K. McGuire, Javed Butler, Gregg C. Fonarow, Mujeeb Basit, Vaishnavi Kannan, Justin L. Grodin, Brendan Everett, Duwayne Willett, Jarett Berry, Ambarish Pandey
Diabetes Care Dec 2019, 42 (12) 2298-2306; DOI: 10.2337/dc19-0587
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