RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.

ABSTRACT:

Objectives

Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce annotation costs, but typical heuristics do not directly optimize downstream prediction. Our goal was to develop a framework that directly uses downstream prediction performance as feedback to guide phenotype correction and sample selection under constrained labeling budgets.

Materials and methods

We propose reinforcement-enhanced label-efficient active phenotyping (RELEAP), a reinforcement learning-based active learning framework. Reinforcement-enhanced label-efficient adaptively integrates multiple querying strategies and, unlike prior methods, updates its policy based on feedback from downstream

SUBMITTER: Yang Y 

PROVIDER: S-EPMC12918302 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature