# Dynamic feature selection in medical predictive monitoring by reinforcement learning

arXiv: https://arxiv.org/abs/2405.19729 · HTML: https://mlsanity.com/paper/2405.19729/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Relief-Based Feature Selection: Introduction and Review](https://mlsanity.com/paper/1711.08421/) (2017)
- [Deep Reinforcement Learning for Cost-Effective Medical Diagnosis](https://mlsanity.com/paper/2302.10261/) (2023)
- [Adaptive Classification for Prediction Under a Budget](https://mlsanity.com/paper/1705.10194/) (2017)
- [Cost-Sensitive Feature Selection by Optimizing F-Measures](https://mlsanity.com/paper/1904.02301/) (2019)

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

- [P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Records](https://mlsanity.com/paper/2508.08646/) (2025)

## Abstract (arXiv)

In this paper, we investigate dynamic feature selection within multivariate time-series scenario, a common occurrence in clinical prediction monitoring where each feature corresponds to a bio-test result. Many existing feature selection methods fall short in effectively leveraging time-series information, primarily because they are designed for static data. Our approach addresses this limitation by enabling the selection of time-varying feature subsets for each patient. Specifically, we employ reinforcement learning to optimize a policy under maximum cost restrictions. The prediction model is subsequently updated using synthetic data generated by trained policy. Our method can seamlessly integrate with non-differentiable prediction models. We conducted experiments on a sizable clinical dataset encompassing regression and classification tasks. The results demonstrate that our approach outperforms strong feature selection baselines, particularly when subjected to stringent cost limitations. Code will be released once paper is accepted.