Filling the gaps in a patient’s medical data
MIT researchers have developed a model that can assimilate multiple types of a patient’s health data to help doctors make decisions with incomplete information. The field of “predictive analytics” holds promise for many health care applications. Machine learning models can be trained to look for patterns in patient data to predict a patient’s risk for disease or dying in the ICU, to aid in sepsis care, or to design safer chemotherapy regimens. The process involves predicting variables of interest, such as disease risk, from known variables, such as symptoms, biometric data, lab tests, and body scans. However, that patient data can come from several different sources and is often incomplete. For example, it might include partial information from health surveys about physical and mental well-being, mixed with highly complex data comprising measurements of heart or brain function. Using machine learning to analyze all available data could help doctors better diagnose and treat...