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Stanford Researchers Use Big Data to Identify High-Risk Cholesterol Patients

February 3, 2015
by Rajiv Leventhal
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Researchers at the Stanford University School of Medicine have started a new project that will use big data and software to identify patients at risk of high-cholesterol disorder.

Specifically, the project is designed to identify Stanford patients who may have a genetic disease that causes a deadly buildup of cholesterol in their arteries. Researchers will comb through electronic health records (EHRs) to identify patients at risk of familial hypercholesterolemia, which often goes undiagnosed until a heart attack strikes.

The project is part of a larger initiative called FIND FH (Flag, Identify, Network, Deliver), a collaborative effort involving Stanford Medicine, Amgen Inc., and the nonprofit Familial Hypercholesterolemia Foundation to use innovative technologies to identify individuals with the disorder who are undiagnosed, untreated, or undertreated. The larger initiative is being funded by Amgen, a biotechnology firm that is developing an experimental cholesterol-lowering drug. The Stanford project is receiving additional funding from the American Heart Association.

For the Stanford project, researchers will use methods to “teach” a computer how to recognize a pattern in the electronic records of Stanford patients who have been diagnosed with FH. The computer will then be instructed to analyze Stanford patient records, for signs of the pattern. The researchers will then report their findings to the patients’ personal physicians, who can encourage screening and therapy. 

 “This disorder certainly leads to premature death in thousands of Americans each year,” Joshua Knowles, M.D., Ph.D., assistant professor of cardiovascular medicine, said in statement. Knowles will lead the effort with Nigam Shah, Ph.D., assistant professor of biomedical informatics, and Ken Mahaffey, M.D., professor of cardiovascular medicine. “Less than 10 percent of cases are diagnosed, leaving an estimated 600,000 to 1 million people undiagnosed. If found early enough and treated aggressively with statin-based regimens, people can live longer, healthier lives,” Knowles said.

Machine learning, in which computer algorithms learn to recognize patterns within data, is widely used by Internet businesses such as Amazon and Netflix to improve customer experience, get information about trends, identify likes and dislikes and target advertisements, Knowles said. “These techniques have not been widely applied in medicine, but we believe that they offer the potential to transform health care, particularly with the increased reliance on electronic health records,” he said.

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