Machine learning for cardiovascular disease improves when social and environmental factors are included


Newswise – Machine learning can accurately predict cardiovascular disease and guide treatment – but models that integrate social determinants of health better capture risks and outcomes for various groups, according to a new study by researchers at the New York University Tandon School of Engineering and NYU School of Global Public Health. The article, published in the American Journal of Preventive Medicine, also highlights the possibilities of improving the way social and environmental variables are taken into account in machine learning algorithms.

Cardiovascular disease is responsible for almost a third of all deaths worldwide and disproportionately affects disadvantaged socio-economic groups. The increase in cardiovascular disease and death is attributed, in part, to social and environmental conditions – also known as social determinants of health – that influence diet and exercise.

“Cardiovascular disease is on the increase, especially in low- and middle-income countries and among communities of color in places like the United States,” said Rumi chunara, associate professor of computer science and engineering at the NYU Tandon School of Engineering and biostatistics at the NYU School of Global Public Health, as well as the lead author of the study. “Because these changes occur over such a short period of time, it is well known that our changing social and environmental factors, such as the increase in processed foods, are causing this change, as opposed to genetic factors. that would change over much longer time scales. . “

Machine learning – a type of artificial intelligence used to detect patterns in data – is growing rapidly in cardiovascular research and care to predict disease risk, incidence and outcomes. Already, statistical methods are essential for assessing cardiovascular disease risk and US prevention guidelines. The development of predictive models provides healthcare professionals with actionable information by quantifying a patient’s risk and guiding the prescription of drugs or other preventative measures.

The risk of cardiovascular disease is usually calculated using clinical information, such as blood pressure and cholesterol levels, but rarely takes into account social determinants, such as neighborhood-level factors. Chunara and his colleagues sought to gain a better understanding of how social and environmental factors begin to be incorporated into machine learning algorithms for cardiovascular disease – which factors are taken into account, how they are analyzed, and which methods improve these models.

“Social and environmental factors have complex and non-linear interactions with cardiovascular disease. Machine learning can be particularly useful in capturing these complex relationships.

– Rumi Chunara, Associate Professor of Computer Science and Engineering

Researchers analyzed existing research on machine learning and cardiovascular disease risk, reviewing over 1,600 articles and ultimately focusing on 48 peer-reviewed studies published in journals between 1995 and 2020.

They found that the inclusion of social determinants of health in machine learning models improved the ability to predict cardiovascular outcomes like readmission, heart failure, and stroke. However, these models generally did not include the full list of community or environmental variables that are important in cardiovascular disease risk. Some studies included additional factors such as income, marital status, social isolation, pollution, and health insurance, but only five studies considered environmental factors such as the ability to walk in a community. and the availability of resources such as grocery stores.

The researchers also noted the lack of geographic diversity in the studies, as the majority used data from the United States, countries in Europe and China, neglecting many areas of the world with an increase in cardiovascular disease.

“If you only do research in places like the United States or Europe, you will not see how social determinants and other environmental factors related to cardiovascular risk interact in different contexts and the knowledge generated will be limited. “said Chunara.

“Our study shows that it is possible to more systematically and comprehensively integrate the social determinants of health into models for statistically predicting cardiovascular disease risk,” said Stephanie Cook, assistant professor of biostatistics at NYU School of Global Public Health and study author. “In recent years, there has been an increasing emphasis on capturing data on the social determinants of health – such as employment, education, nutrition and social support – in the files of electronic health, which creates an opportunity to use these variables in machine learning studies and further improve the performance of risk prediction, especially for vulnerable groups.

“Including the social determinants of health in machine learning models can help us unravel the causes of disparities and draw attention to areas where we need to intervene in the risk structure,” Chunara added. “For example, it can improve clinical practice by helping healthcare professionals identify patients in need of referral to community resources such as housing services and greatly enhances the complex synergy between individual health and our resources. environmental.

In addition to Chunara and Cook, study authors include Yuan Zhao, Erica Wood, and Nicholas Mirin, students at the NYU School of Global Public Health. The research was funded by funding from the National Science Foundation (IIS-1845487).

About New York University Tandon School of Engineering

The NYU Tandon School of Engineering dates from 1854, when the New York University School of Civil Engineering and Architecture and the Brooklyn Collegiate and Polytechnic Institute (better known as Brooklyn Poly) were founded. A January 2014 merger created a comprehensive school of teaching and research in engineering and applied sciences, rooted in a tradition of invention and entrepreneurship and dedicated to the advancement of technology in the service of society. In addition to its main location in Brooklyn, NYU Tandon collaborates with other schools within NYU, one of the nation’s leading private research universities, and is closely linked to the engineering programs of NYU Abu Dhabi and NYU Shanghai. . It operates Future Labs focused on start-ups in downtown Manhattan and Brooklyn and an award-winning online graduate program. For more information visit

About the NYU School of Global Public Health

At the NYU School of Global Public Health (NYU GPH), we are preparing the next generation of public health pioneers with the critical thinking skills, acumen, and entrepreneurial approaches needed to reinvent the public health paradigm. Dedicated to the use of a non-traditional interdisciplinary model, NYU GPH aims to improve health around the world through a unique blend of study, research and practice in global public health. The school is located in the heart of New York City and spans NYU’s global network across six continents. Innovation is at the heart of our ambitious approach, thinking and teaching. To learn more, visit:

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