Precision Medicine and Machine Learning

February 13, 2019
3 pm

Michael R. Kosorok, PhD, W. R. Kenan, Jr. Distinguished Professor and Chair of Biostatistics, University of North Carolina at Chapel Hill

Precision medicine, the paradigm of improving clinical care through data driven approaches to tailoring treatment to the individual, is an important area of statistical and biomedical research. Individualized treatment rules (ITRs) formalize precision medicine as mappings from the space of patient covariates to the set of available treatments or, equivalently, as mappings which identify covariate-defined subgroups for which different treatments should be applied. ITRs are thus an important tool to improve patient outcomes through utilizing biomarkers to target treatment. Machine learning has become an increasingly utilized and evolving methodology for ITR discovery, and we discuss recent progress in this area and present examples in type I diabetes and bipolar disorder. Some theoretical guarantees are also presented.

Event Type: 
Biostatistics and Bioinformatics Seminar