New Artificial Intelligence Program Could Help Treat Hypertension
For the nearly half of Americans with hypertension, it’s a potential death sentence—close to 700,000 deaths in 2021 were caused by high blood pressure, according to the US Centers for Disease Control and Prevention. It also increases the risk of stroke and chronic heart failure. But while it’s relatively easy to prevent or moderate if caught early—eat well, exercise more, drink less—it can be tough to treat. Although physicians have a bevy of potential hypertension medications to choose from, each is littered with pros and cons, making prescribing the most effective one a challenge: beta-blockers slow the heart, but can cause asthma; ACE inhibitors relax blood vessels, but can lead to a hacking cough. Now, a new artificial intelligence program may help doctors better match the right medicines to the right patients.
The data-driven model, codeveloped by College of Engineering professor Ioannis Paschalidis, aims to give clinicians real-time hypertension treatment recommendations based on patient-specific characteristics, including demographics, vital signs, past medical history, and clinical test records. The model, described in a recent study published in BMC Medical Informatics and Decision Making, has the potential to help reduce systolic blood pressure—measured when the heart is beating rather than resting—more effectively than the current standard of care. According to the researchers, the program’s approach to transparency could also help improve physicians’ trust in artificial intelligence–generated results.