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Precision medicine is an emergent paradigm for health care research and delivery. Its primary goal is to improve clinical outcomes by developing treatments on the basis of each patient's individual characteristics. One focus within precision medicine is the problem of patient stratification, which is the method by which patients are partitioned into clinically meaningful subgroups. This is both a medical and mathematical problem, as it requires the identification of structures (i.e., clusters) in clinical data. Topological data analysis (TDA) is an emergent approach that uses topology --- a field of mathematics that studies shapes and the relationships between them --- to examine structure in data. The TDA algorithm Mapper converts data into graphs in order to create a sparse representation of the data. TDA has an important role to play at the intersection of precision medicine and big data. In this talk. We describe the successes and shortcomings of Mapper in precision medicine applications. These shortcomings motivate our development of the MapperPlus algorithm, which converts Mapper graphs into disjoint clusters. We apply MapperPlus to the problem of risk-stratifying patients suffering from myocardial infarction.

Click to join via Zoom: https://ucdavis.zoom.us/j/93501618035