New research shows how a machine-learning technique could provide insight into how to find the patients that would benefit the most from treatment for hypertension.
The study, which came out of UCLA, describes how a machine-learning technique known as “casual forest” could determine the hypertension patients that would benefit the most from treatment rather than assuming that the highest-risk patients require the most clinical attention.
According to the Centers for Disease Control and Prevention (CDC), over 670,000 deaths in the US can be attributed annually to hypertension. In addition, while about 47 percent of US adults have hypertension, only 24 percent of this population has the condition under control.
Traditionally, clinicians treating patients with high blood pressure focus on those with the highest risk of poor outcomes, as the assumption is that they will require the highest level of treatment. The researchers set out to see if they could leverage AI to treat patients based on benefit rather than risk for improved outcomes. They found their solution in a new ML technique, coined “casual forest.”
The study included data from 10,672 participants, all of whom were randomized to systolic blood pressure (SBP) targets of either less than 120 mmHg or less than 140 mmHg from two randomized controlled trials.
The researchers used the casual forest technique to create a prediction model of individualized treatment effects related to the control of SBP and its correlation with reductions in adverse cardiovascular outcomes after three years.
They found that 78.9 percent of individuals with an SBP greater than 130 mmHg achieved benefits from intensive SBP control.
“We found that a substantial number of individuals without hypertension benefited from lowering their blood pressure,” said lead author Kosuke Inoue, MD, Ph.D., who undertook the study while an epidemiology graduate student at the UCLA Fielding School of Public Health and is now an associate professor of social epidemiology at Kyoto University, in a press release. “By applying the causal forest method, we found that treating individuals with high estimated benefits provided better population health outcomes than the traditional high-risk approach.”
Further, the researchers noted that high-benefit approaches could increase the efficacy associated with treatment, potentially being more reliable compared to high-risk approaches.
How BigRio Helps Bring Advanced AI Solutions to Healthcare
As the UCLA researchers have discovered, improving disease detection and making better decisions on the allocation of medical resources is an area where AI and machine learning are making a huge impact in healthcare.
BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve treatment and medical outcomes. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to provide earlier detection of serious medical conditions.
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You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.
Rohit Mahajan is a Managing Partner with BigRio. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.
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