RELEAP:用于电子健康记录的强化增强的标签效率主动表型化
Yang Yang1, Kathryn I Pollak2,3, Bibhas Chakraborty1
1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, United States.
JAMIA open
|February 23, 2026
概括
这项研究引入了一个新的积极学习框架,RELEAP,以改善电子健康记录 (EHR) 现型化,用于风险预测. 通过使用下游模型性能来指导表型修正和样本选择,RELEAP提高了预测准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
- 临床决策支持 临床决策支持
背景情况:
- 电子健康记录 (EHR) 现型化通常使用杂的代理标签,影响风险预测可靠性.
- 现有的主动学习方法不能直接优化下游预测性能.
研究的目的:
- 开发一个使用下游预测性能作为反来纠正表型和选择样本的框架.
- 在有限的标签预算下,提高基于EHR的风险预测的可靠性.
主要方法:
- 提议的强化增强的标签效率主动表型 (RELEAP),一个基于强化学习的主动学习框架.
- RELEAP适应性地整合了查询策略,并根据下游模型反更新了其政策.
- 在杜克大学卫生系统队列上评估RELEAP,用于使用后勤回归和惩罚Cox生存模型预测发生的肺癌风险.
主要成果:
- 与仅代理的基线相比,RELEAP的性能有所改善,并且接近Oracle的性能.
- 后勤AUC从0.774增加到0.807;生存一致性指数从0.715增加到0.749.
- 性能增长在代中稳定,在性别分层分析中一致.
结论:
- RELEAP将表型精细化与预测结果联系起来,确定改善下游歧视和校准的样本.
- 为固定的积极学习规则提供了一个原则性的替代方案.
- 为基于EHR的风险预测提供了一个可扩展,标签效率高的范式,减少手工审查并提高可靠性.
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