Machine learning screens patients for life-threatening genetic disease

Using large healthcare encounter datasets, a machine learning algorithm is able to identify patients with a common genetic disorder that carries a high risk for early heart attacks and strokes. While individuals with familial hypercholesterolaemia (FH) have 20 times the risk of developing cardiovascular disease than the general population, fewer than 10 percent of the 1.3 million Americans born with the genetic disease are diagnosed. "People born with familial hypercholesterolemia develop cardiovascular damage by puberty, often culminating in early heart attacks or the need for surgery as young or middle-aged adults," says Katherine Wilemon, founder and CEO of the FH Foundation, a non-profit research and advocacy organization. "Since diagnosis of this deadly but treatable condition has stalled in the American medical system, the FH Foundation harnessed artificial intelligence and big data to accelerate identification of those most likely to have FH." In a new study, a machine learning model created by the FH Foundation successfully leveraged healthcare encounter databases to identify individuals with the genetic disorder.