Big players in software are putting their weight behind artificial intelligence as a way to improve health care decision making. New computer models stretch the limits on how early doctors can spot disease, or how quickly molecular compounds can be screened for use as new drugs.
In the past week, news of several such models have been released, which we’ve rounded up for you here. IBM announced a computational model that predicts heart failure, Stanford University reported a deep learning algorithm that predicts the safety of drug compounds, and Intel announced a competition to find an algorithm for early detection of lung cancer.
IBM model predicts heart failure
IBM Research, the innovation arm of multinational tech giant IBM, and collaborators have developed a machine learning model that predicts heart failure up to two years before a patient would typically be diagnosed. The researchers trained the model using hidden signals gleaned from electronic health records and doctors’ notes.
Heart failure—a chronic condition in which the heart muscle isn’t strong enough to pump enough blood to meet the body’s needs—is hard to predict. In fact most people don’t know they have a problem until they land in the hospital. “By the time a patient is diagnosed, very often an acute event has happened and irreversible damage has been caused,” says Jianying Hu, who led the development of the model and is a program director at IBM Research’s Center for Computational Health.
Hu’s group wondered if they could predict the problem well before a person ends up in the hospital. To do that, the group took a fresh analytical look at electronic health data that is routinely collected at doctor visits. “We found that diagnoses of other conditions, medication, and hospitalization records, in that order, provide the most valuable signal for predicting heart failure,” she says. They also mined key information from doctors’ notes using natural language processing techniques.
The data came from the health records of more than 10,000 people. The model was highly accurate at predicting heart failure up to one year in advance and “degraded gracefully” in accuracy until about two years ahead of a problem, says Hu. “Clearly there’s power in the data that is routinely collected in the care process,” she says.
