Machine learning can accurately predict cardiovascular disease and guide treatment--but models that incorporate social determinants of health better capture risk and outcomes for diverse groups, finds a new study by researchers at New York University's School of Global Public Health and Tandon School of Engineering. The article, published in the American Journal of Preventive Medicine, also points to opportunities to improve how social and environmental variables are factored into machine learning algorithms.
Cardiovascular disease is responsible for nearly a third of all deaths worldwide and disproportionately affects lower socioeconomic groups. Increases in cardiovascular disease and deaths are attributed, in part, to social and environmental conditions--also known as social determinants of health--that influence diet and exercise.
"Cardiovascular disease is increasing, particularly in low- and middle-income countries and among communities of color in places like the United States," said Rumi Chunara, associate professor of biostatistics at NYU School of Global Public Health and of computer science and engineering at NYU Tandon School of Engineering, as well as the study's senior author. "Because these changes are happening over such a short period of time, it is well known that our changing social and environmental factors, such as increased processed foods, are driving this change, as opposed to genetic factors which would change over much longer time scales."
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Machine learning--a type of artificial intelligence used to detect patterns in data--is being rapidly developed in cardiovascular research and care to predict disease risk, incidence, and outcomes. Already, statistical methods are central in assessing cardiovascular disease risk and U.S. prevention guidelines. Developing predictive models gives health professionals actionable information by quantifying a patient's risk and guiding the prescription of drugs or other preventive measures.
Cardiovascular disease risk is typically computed using clinical information, such as blood pressure and cholesterol levels, but rarely take social determinants, such as neighborhood-level factors, into account. Chunara and her colleagues sought to better understand how social and environmental factors are beginning to be integrated into machine learning algorithms for cardiovascular disease--what factors are considered, how they are being analyzed, and what methods improve these models.