Using Machine Learning to Target Behavioral Health Interventions

Traditional risk modeling often just considers claims data and uses between five and seven variables to tell the user who needs attention. In another one of our projects which is around preventing hospital admissions for diabetics, our highest-performing models are considering 30 different data sources, structured and unstructured, that add up to 438 different variables. That gives us some impression about the potential of machine learning and why folks are so excited about it.