Learning to Ask Medical Questions using Reinforcement Learning

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. T...

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Bibliographic Details
Published in:arXiv.org 2020-05
Main Authors: Shaham, Uri, Zahavy, Tom, Caraballo, Cesar, Mahajan, Shiwani, Massey, Daisy, Krumholz, Harlan
Format: Article
Language:English
Subjects:
Algorithms
Learning
Markov analysis
Markov processes
Online Access:Get full text
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Summary: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}.
ISSN:2331-8422