Workshop in Biostatistics

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
Electrical Engineering, Stanford


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.

Suggested reading:

Lai and Robbins. "Asymptotically efficient adaptive allocation rules.Advances in applied mathematics (1985).

Li et al. "A contextual-bandit approach to personalized news article recommendation.WWW (2010).

Kim et al. "The BATTLE trial: personalizing therapy for lung cancer.Cancer discovery (2011).