Michael R. Kosorok
Title: Precision Medicine and Machine Learning
Abstract: There has recently been an explosion of interest and activity in personalized medicine. However, the goal of personalized medicine—wherein treatments are targeted to take into account patient heterogeneity—has been a focus of medicine for centuries. Precision medicine, on the other hand, is a much more recent refinement which seeks to develop personalized medicine that is empirically based, scientifically rigorous, and reproducible. In this presentation, we describe several new machine learning developments which advance this quest through discovering individualized treatment rules based on patient-level features. Regression and classification are useful statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning approaches can help with this because of their ability to artfully handle high dimensional feature spaces with potentially complex interactions. For the multiple decision setting, reinforcement learning, which is similar to but different from regression, is necessary to properly account for delayed effects. There are several other intriguing nonstandard machine learning tools which can also greatly facilitate discovery of treatment rules. One of these is outcome weighted learning, or O-learning, which directly estimates the decision rules without requiring regression modeling and is thus robust to model misspecification. Several clinical examples illustrating these approaches will also be given.