According to a study conducted by the University of Utah Health, artificial intelligence (AI) could lead to improved methods of predicting cardiovascular disease. According to the researchers, the comprehensive approach used could assist physicians in anticipating, preventing, or treating serious heart problems, possibly even before a patient is aware of the underlying condition. Despite the fact that the research focused on cardiovascular disease, the findings could usher in a new era of personalized, preventive medicine.
The study sorted through more than 1.6 million electronic health records using machine learning software, which helped identify the comorbidities most likely to aggravate a specific medical condition, such as cardiovascular disease. The researchers used a type of artificial intelligence known as probabilistic graphical networks (PGM) to calculate how any combination of these comorbidities could affect the risks associated with heart transplants, congenital heart disease, or sinoatrial node dysfunction. The research was published in the online journal PLOS Digital Health. According to the researchers, the study could pave the way for artificial intelligence to help refine the risk for virtually every medical diagnosis, from cancer to diabetes.
In addition to Drs. Tristani-Firouzi and Yandell, University of Utah Health scientists contributing to this research were S. Wesolowski, G. Lemmon, E.J. Hernandez, A. Henrie, T.A. Miller, D. Wyhrauch, M.D. Puchalski, B.E. Bray, R.U. Shah, V.G. Deshmukh, R. Delaney, H.J. Yost, and K. Eilbeck.
The study was supported by the AHA Children’s Strategically Focused Research Network, the Nora Eccles Treadwell Foundation, and the National Heart, Lung and Blood Institute.
Source: Public Library of Science (“PLOS”) (link)
Images: MidJourney, Prompts by Lewis Farrell
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