Objective: 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 most rely on fixed heuristics and do not ensure that phenotype refinement improves prediction performance. 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. RELEAP adaptively integrates multiple querying strategies and, unlike prior methods, updates its policy based on feedback from downstream models. We evaluated RELEAP on a de-identified Duke University Health System (DUHS) cohort (2014-2024) for incident lung cancer risk prediction, using logistic regression and penalized Cox survival models. Performance was benchmarked against noisy-label baselines and single-strategy active learning. Results: RELEAP consistently outperformed all baselines. Logistic AUC increased from 0.774 to 0.805 and survival C-index from 0.718 to 0.752. Using downstream performance as feedback, RELEAP produced smoother and more stable gains than heuristic methods under the same labeling budget. Discussion: By linking phenotype refinement to prediction outcomes, RELEAP learns which samples most improve downstream discrimination and calibration, offering a more principled alternative to fixed active learning rules. Conclusion: RELEAP optimizes phenotype correction through downstream feedback, offering a scalable, label-efficient paradigm that reduces manual chart review and enhances the reliability of EHR-based risk prediction.

翻译：目的：电子健康记录（EHR）表型分析通常依赖于噪声代理标签，这削弱了下游风险预测的可靠性。主动学习可以降低标注成本，但多数方法依赖固定启发式规则，且无法确保表型优化能提升预测性能。我们的目标是开发一个框架，在有限标注预算下，直接利用下游预测性能作为反馈来指导表型校正与样本选择。材料与方法：我们提出强化增强型标签高效主动表型分析（RELEAP），一种基于强化学习的主动学习框架。RELEAP自适应地整合多种查询策略，与先前方法不同，其策略会根据下游模型的反馈进行更新。我们在杜克大学医疗系统（DUHS）2014-2024年去标识化队列中，针对新发肺癌风险预测任务评估RELEAP，使用逻辑回归和惩罚Cox生存模型。性能以噪声标签基线和单策略主动学习方法为基准进行比较。结果：RELEAP在所有基准测试中均表现更优。逻辑回归AUC从0.774提升至0.805，生存模型C指数从0.718提升至0.752。通过将下游性能作为反馈，RELEAP在相同标注预算下实现了比启发式方法更平滑、更稳定的性能增益。讨论：通过将表型优化与预测结果关联，RELEAP能够学习哪些样本最能提升下游判别与校准能力，为固定主动学习规则提供了更具原则性的替代方案。结论：RELEAP通过下游反馈优化表型校正，提供了一种可扩展、标签高效的范式，减少了人工病历审查工作量，并增强了基于EHR的风险预测的可靠性。