Automating artificial intelligence for medical decision-making

MIT CSAIL researchers are hoping to accelerate the use of artificial intelligence to improve medical decision-making, by automating a key step that's usually done by hand -- and that's becoming more laborious as certain datasets grow ever-larger. The field of predictive analytics holds increasing promise for helping clinicians diagnose and treat patients. Machine-learning models can be trained to find patterns in patient data to aid in sepsis care, design safer chemotherapy regimens, and predict a patient's risk of having breast cancer or dying in the ICU, to name just a few examples. Typically, training datasets consist of many sick and healthy subjects, but with relatively little data for each subject. Experts must then find just those aspects -- or "features" -- in the datasets that will be important for making predictions.