Active Learning for Developing Personalized Treatment
This work addresses the high cost of clinical trials for personalized medicine, offering a method to reduce expenses and time, though it is incremental in applying existing active learning concepts to this domain.
The paper tackles the problem of designing efficient clinical trials for personalized treatment by using active learning techniques to optimize recruitment, timing, and treatment assignment, and evaluates these policies with simulated and depression trial data, showing improved efficiency compared to other methods.
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 paper explores 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 minimax bandit model with two different optimization criteria, 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.