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.

研究采用强化学习优化时间序列场景下的动态特征选择方法，提高临床预测监测中的数据挖掘效果。