Abstract:
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In the past decade there has been an explosion of interest and activity in personalized medicine. The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions. For the multiple decision setting, reinforcement learning, a type of machine learning that is neither regression nor classification, is necessary to properly account for delayed effects. There are several other intriguing nonstandard machine learning tools which can greatly facilitate discovery of decision rules. In this talk, we will discuss the benefits of machine learning in personalized medicine as well as new developments in machine learning inspired by the personalized medicine quest.
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