结合学习,以提高使用电子健康记录准确识别眼患者
Tushar Mungle1, Behzad Naderalvojoud1, Chris A Andrews2
1Department of Medicine, Stanford University, Stanford, CA 94305, United States.
JAMIA open
|August 13, 2025
概括
整合结构化和非结构化健康数据可以在现实世界数据集 (RWD) 中改善青光眼的识别. 与传统方法相比,多式联动组合模型显示出更高的性能和公平性,提高了患者管理.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 在现实世界数据集 (RWD) 中用于临床表型识别的眼科研究传统上仅依赖于结构化数据元素 (SDE).
- 这种依赖限制了全面了解和识别像眼这样复杂的疾病.
- 需要探索利用各种数据源的先进方法,以改善患者识别.
研究的目的:
- 评估综合SDE和自由文本数据的多式组合模型的性能,通用性和公平性,以识别青光眼患者.
- 将这些多式联运模型与仅使用SDE的模型进行比较.
- 评估整合非结构化电子健康记录 (EHR) 数据对临床表型发现的影响.
主要方法:
- 一项回顾性横截面研究,涉及2012-2021年期间来自两个卫生系统 (密歇根大学和斯坦福大学) 的1728名患者.
- 来自BioClinicalBERT的自由文本嵌入与SDE结合在一起. 使用编辑近邻 (ENN) 低采样和边界合成少数群体超采样技术 (bSMOTE) 来解决阶级不平衡.
- 包括拉索回归,随机森林,支向量分类器和包装组合方法在内的各种模型得到了训练和外部验证. 公平性是通过均等赔率差异 (EOD) 和目标概率差异 (TPD) 来评估的.
主要成果:
- 密歇根大学的多模式拉索回归 (LR) 在F1分数和AUROC.中优于单模式随机森林 (RF) 和ICD编码方法.
- 包装组合方法 (BM) 取得了卓越的性能,在训练场上F1为83.02和AUROC为97.59.
- 在外部验证期间,BM获得了最高的F1 (68.47),超过了单模和多模LR型号. BM还表明,性别,种族和种族之间的公平差距较低.
结论:
- 整合结构化和非结构化电子心脏病记录数据的多模组模型显著优于传统的仅使用SDE模型进行青光眼的识别.
- 这些模型实现了跨人口亚组的公平预测,解决了现有方法的关键局限性.
- 开发的方法增强了使用RWD的表型发现,为改善眼科患者管理和临床结果铺平了道路.
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