Learning to Ask Medical Questions using Reinforcement Learning
Abstract
We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection. Given a masked vector of input features, a reinforcement learning agent iteratively selects certain features to be unmasked, and uses them to predict an outcome when it is sufficiently confident. The algorithm makes use of a novel environment setting, corresponding to a non-stationary Markov Decision Process. A key component of our approach is a guesser network, trained to predict the outcome from the selected features and parametrizing the reward function. Applying our method to a national survey dataset, we show that it not only outperforms strong baselines when requiring the prediction to be made based on a small number of input features, but is also highly more interpretable. Our code is publicly available at \url{https://github.com/ushaham/adaptiveFS}.
Cite
@article{arxiv.2004.00994,
title = {Learning to Ask Medical Questions using Reinforcement Learning},
author = {Uri Shaham and Tom Zahavy and Cesar Caraballo and Shiwani Mahajan and Daisy Massey and Harlan Krumholz},
journal= {arXiv preprint arXiv:2004.00994},
year = {2020}
}