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Abstract Details

Activity Number: 105
Type: Invited
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #303640
Title: Active Learning for Developing Personalized Treatment
Author(s): Kun Deng*+ and Joelle Pineau and Susan Murphy
Companies: University of Michigan and McGill University and University of Michigan
Address: University of Michigan, Ann Arbor, MI, 48109,
Keywords: machine learning ; active learning ; personalized medicine ; bandit model ; reinforcement learning ; biomarker profile

The personalization of treatment via biomarkers 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. We explore the use of active learning techniques to design more efficient trials, addressing 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 novel minimax bandit model and discuss 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 contrast our methods with other plausible active learning policies.

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