# Learning to Ask Medical Questions using Reinforcement Learning

arXiv: https://arxiv.org/abs/2004.00994 · HTML: https://mlsanity.com/paper/2004.00994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation](https://mlsanity.com/paper/2508.05677/) (2025)

## Abstract (arXiv)

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 urlhttps://github.com/ushaham/adaptiveFS.