Active Learning for Developing Personalized Treatment

摘要

The personalization of treatment via bio markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This pa per explores the use of active learning tech niques to design more efficient trials, address ing issues such as whom to recruit, at what point in the trial, and which treatment to assign, throughout the duration of the trial. We propose a minimax bandit model with two different optimization criteria, and dis cuss the computational challenges and issues pertaining to this approach. We evaluate our active learning policies using both simulated data, and data modeled after a clinical trial for treating depressed individuals, and con trast our methods with other plausible active learning policies.