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Healthcare is rapidly becoming a data-intensive discipline, driven by increasing digitization of health data, novel measurement technologies, and new policy-based incentives. Critical decisions about whom and how to treat can be made more precisely by layering an individual’s data over that from a population. In my laboratory, we develop new classes of computational diagnostic and treatment planning tools—tools that tease out subtle information from “messy” observational datasets, and provide reliable inferences given detailed context about the individual patient. I will give example disease areas where such tools are already beginning to show translational impact. In context, I will describe challenges associated with learning models from these data and new techniques that leverage probabilistic methods and counterfactual reasoning for tackling the aforementioned challenges.
