Machine learning in precision diabetes care and cardiovascular risk prediction
Open accessAbstract
Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
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- Authors
- Oikonomou, E. K., & Khera, R.
- Affiliations
- Yale University (US)
- Yale New Haven Hospital (US)
- Corresponding author
- Khera, Rohan
- Venue
- Cardiovascular Diabetology
- Published
- 2023-09-25
- Volume
- 22
- Issue
- 1
- Pages
- 259-259
- ISSN
- 1475-2840 (ISSN-L)
- PMC
- PMC10521578
- OpenAlex
- W4387012889
- Cited by
- 190
- References
- 24
- Citations by year
- 2026: 65 · 2025: 86 · 2024: 34 · 2023: 5
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Cardiovascular Diabetology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Heart, Lung, and Blood Institute, United States (F32HL170592, K23HL153775)
- Doris Duke Charitable Foundation,United States (2022060)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- machine learning, artificial intelligence, diabetes mellitus, cardiovascular risk prediction, precision medicine, diabetes diagnosis, outcome prediction, phenotyping, diabetes treatment, cardiovascular complications, algorithmic bias, health equity, regulatory framework
- MeSH terms
- Machine Learning, Heart Disease Risk Factors, Artificial Intelligence, Cardiovascular Diseases, Diabetes Mellitus, Humans, Risk Factors
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