Earlier this month, the American Heart Association and the Duke Clinical Research Institute announced their partnership to develop and test machine-learning techniques in the research and treatment of cardiovascular disease.

“Together, we will develop new methods and technology for analyzing data, and create abundant opportunities for trainees to excel,” said Jennifer Hall, chief of the AHA’s Institute for Precision Cardiovascular Medicine, in a press release. “We’re pleased to work with the DCRI’s team of data science experts to develop and test emerging strategies to power machine learning.”

AHA’s Institute for Precision Cardiovascular Medicine and DCRI’s science team will use the association’s Precision Medicine Platform—“a cloud-based data resource that revolutionizes how the research community accelerates solutions for cardiovascular diseases and stroke”—to do this work. The platform contains cardiovascular health-related data from DCRI, AstraZeneca, and Intermountain Healthcare, among others.

“This allows us to bring data from all different sources together, which makes it easier for scientists to get access to the data,” said Hall in an interview with Modern Healthcare.

So far, AHA’s platform contains data from 23 million people, and this new partnership will research ways big data is “managed, accessed, harmonized, searched and deposited, specific to secondary analyses of clinical databases,” according to the release. The two organizations will also use grants to create new machine learning tools and aggregated data repositories.

“There is great potential in machine learning and other artificial intelligence methods to discover new insights, but we have to be sensible and think clearly about how we use it,” said Michael Pencina, DCRI’s director of biostatistics and a professor of biostatistics and bioinformatics at the Duke School of Medicine, in a press release. “I think team science is the key that unlocks that potential.”

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