|DATE:||October 13, 2016|
|TIME:||1:30 - 2:50 pm|
|LOCATION:||Medical School Office Building, Rm x303|
|TITLE:||Online Decision-Making with High-Dimensional Covariates|
Hamsa Sridhar Bastani
Big data has enabled decision-makers to tailor treatment decisions for patients based on their clinical information. This involves learning a model of decision rewards conditional on individual patient covariates. In many healthcare settings, these covariates are high-dimensional; however, typically only a small subset of the observed features are predictive of a decision’s success. We formulate this problem as a multi-armed bandit with high-dimensional covariates, and present a new efficient bandit algorithm based on the LASSO estimator. Our regret analysis establishes that our algorithm achieves near-optimal performance in comparison to an oracle that knows all the problem parameters. Furthermore, we illustrate the practical relevance of our algorithm by evaluating it on a real-world clinical problem of warfarin dosing. A patient’s optimal warfarin dosage depends on the patient’s genetic profile and medical records; incorrect initial dosage may result in adverse consequences such as stroke or bleeding. We show that our algorithm outperforms existing bandit methods as well as physicians to correctly dose a majority of patients.
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