---
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 <https://github.com/ushaham/adaptiveFS>.'
author:
- Uri Shaham
- Tom Zahavy
- Cesar Caraballo
- Shiwani Mahajan
- Daisy Massey
- Harlan Krumholz
bibliography:
- 'questionnaire.bib'
title: Learning to Ask Medical Questions using Reinforcement Learning
---

Introduction {#sec:intro}
============

Feature selection is an important topic in traditional machine learning [@li2018feature], which motivated a large number of widely adopted works, e.g., Lasso [@tibshirani1996regression]. In various cases, the process of obtaining input measurements requires considerable effort 