Learning to Ask Medical Questions using Reinforcement Learning
This work addresses the need for interpretable and efficient feature selection in medical data analysis, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of adaptive feature selection for outcome prediction by proposing a reinforcement learning agent that iteratively selects features to unmask and predict when confident, achieving higher performance and interpretability with a small number of features on a national survey dataset.
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}.