New Healthcare AI Model Facilitates Personalized Medical Treatment of Hypertension
Model helps match people with high blood pressure to the medication most likely to work for them—and could improve trust in healthcare AI
By Maureen Stanton for The Brink
High blood pressure is a major risk factor for heart disease and stroke, two leading causes of death in the U.S, and it is on the rise in this country. Nearly one in two adults have high blood pressure according to the Center for Disease Control. While hypertension is a treatable medical condition, it can be challenging for physicians to treat as an individual’s diet, lifestyle, genetics, biology, and other personal factors can influence the effectiveness of therapy.
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 NSF-funded artificial intelligence program may help doctors better match the right medicines to the right patients.
The data-driven model, codeveloped by Boston University data 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 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.
“This is a new machine learning algorithm leveraging information in electronic health records and showcasing the power of AI in healthcare,” says Yannis Paschalidis, a BU College of Engineering distinguished professor and director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. “Our data-driven model is not just predicting an outcome, it is suggesting the most appropriate medication to use for each patient. Our goal is to facilitate a personalization approach for hypertension treatment based on machine learning algorithms seeking to maximize the effectiveness of hypertensive medications at the individual level.”
The study’s co-principal investigators are Dr. Nick Cordella, Medical Director for Quality and Patient Safety at Boston Medical Center (BMC), and Dr. Rebecca Mishuris, Chief Medical Information Officer and Vice President of Mass General Brigham. The research builds on earlier collaboration with Dr. Bill Adams, Director of the Boston University Clinical and Translational Institute (BU CTSI) and Director of Community Health Informatics for Boston HealthNet.
Read more about this study in The Brink.