The researchers and physicians used electronic health records to identify conditions associated with cardiovascular disease. They then combined the EHR records with a probabilistic graphical network, an AI program, to calculate how many of those conditions would lead a patient to become at-risk for heart disease, heart transplants or sinoatrial node dysfunction.
The study found that the most at-risk adults had a medical history of cardiomyopathy, viral myocarditis or using the heart failure drug milrinone.
Patients who had a combination of these were at greater risk, according to the study.
The researchers said they believe this technology has the potential to help physicians treat, prevent or even foresee if a patient might be at-risk for heart conditions.
