Development and Validation of a Machine Learning-Based Approach to Identify High-Risk Diabetic Cardiomyopathy Phenotype
Machine learning-based techniques may identify 16% to 29% of individuals with diabetes as having a high-risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies.
Authors
- Matthew W. Segar (ORCID: https://orcid.org/0000-0001-6100-0897)
- DuWayne L. Willett (ORCID: https://orcid.org/0000-0002-3087-4193)
- Ambarish Pandey (ORCID: https://orcid.org/0000-0001-9651-3836)
- David Kao (ORCID: https://orcid.org/0000-0002-2832-9348)
- Kershaw V. Patel (ORCID: https://orcid.org/0009-0006-7418-4334)
- James Louis Januzzi (ORCID: https://orcid.org/0000-0002-8338-1798)
- Muhammad Shahzeb Khan (ORCID: https://orcid.org/0000-0003-1250-6351)
- Muhammad Usman (ORCID: https://orcid.org/0000-0001-9747-8892)
- Subodh Verma
- Lakshman Manjunath
- Carolyn S.P. Lam
- Javed Butler
Institutions
- Houston Methodist (US)
- St. Michael's Hospital (CA)
- Harvard University (US)
- National University of Singapore (SG)
- Duke University (US)
- University of Toronto (CA)
- Baylor University Medical Center (US)
- Jackson Memorial Hospital (US)
- University of Mississippi Medical Center (US)
- Massachusetts General Hospital (US)
- National University Heart Centre Singapore (SG)
- Baim Institute for Clinical Research (US)
- Houston Institute for Clinical Research (US)
- National Heart Centre Singapore (SG)
- Duke University Hospital (US)
- Baylor Scott & White Health (US)
- The University of Texas Southwestern Medical Center (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- European Journal of Heart Failure
- Published
- 2024-09-06
- DOI
- https://doi.org/10.1002/ejhf.3443
- Citations
- 6
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 2.41