American Journal of Preventive Cardiology (Dec 2022)
Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology
- Aamir Javaid,
- Fawzi Zghyer,
- Chang Kim,
- Erin M. Spaulding,
- Nino Isakadze,
- Jie Ding,
- Daniel Kargillis,
- Yumin Gao,
- Faisal Rahman,
- Donald E. Brown,
- Suchi Saria,
- Seth S. Martin,
- Christopher M. Kramer,
- Roger S. Blumenthal,
- Francoise A. Marvel
Affiliations
- Aamir Javaid
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Fawzi Zghyer
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Chang Kim
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Erin M. Spaulding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Nino Isakadze
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Jie Ding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Daniel Kargillis
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Yumin Gao
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Faisal Rahman
- Division of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Donald E. Brown
- School of Data Science, University of Virginia, Charlottesville, VA, USA
- Suchi Saria
- Machine Learning and Healthcare Laboratory, Departments of Computer Science, Statistics, and Health Policy, Malone Center for Engineering in Healthcare, and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD, USA
- Seth S. Martin
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Christopher M. Kramer
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, VA, USA
- Roger S. Blumenthal
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
- Francoise A. Marvel
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA; Corresponding author.
- Journal volume & issue
-
Vol. 12
p. 100379
Abstract
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.
