For the nearly half of Americans with hypertension, it’s a potential death sentence; it increases the risk of stroke and chronic heart failure. While it’s relatively easy to prevent or moderate if caught early — eat well, exercise more, consume less alcohol — 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. Now, a new artificial intelligence program may help doctors better match the right medicines to the right patients.
The data-driven model, co-developed by Boston University scientists and physicians, 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 U.S. National Science Foundation-supported 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.
“This is a new machine-learning algorithm leveraging information in electronic health records and showcasing the power of AI in healthcare,” says Ioannis Paschalidis. “Our data-driven model is not just predicting an outcome; it is suggesting the most appropriate medication to use for each patient.”
Currently, when choosing which medication to prescribe a patient, a doctor considers the patient’s history, treatment goals, and the benefits and risks associated with specific medicines. Oftentimes, selecting which drug to prescribe when there are multiple options — and of the options, neither drug is better or worse than the other — can be a bit of a coin toss.
By contrast, the new model generates a custom hypertension prescription using a patient’s profile, giving physicians a list of suggested medications with an associated probability of success. The researchers’ aim was to highlight the treatment that best controls systolic blood pressure for each patient based on its effectiveness in a group of similar patients.
“This work shows the potential of AI research to generate effective real-world solutions to challenging problems like those in health care,” says Wendy Nilsen, an NSF program director.
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