临床知情半监督学习改善了电子健康记录中的疾病注释和公平性:一项玻璃眼病例研究
Mousa Moradi1, Rishi Shah1, Asahi Fujita2
1Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School.
Research square
|November 19, 2025
概括
这项研究介绍了Ci-SSGAN,一种新的AI框架,使用临床笔记来改善疾病特征和公平. 它提高了对玻璃眼等疾病的诊断准确性,优于传统方法.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 眼科 眼科研究 眼科研究
背景情况:
- 结构化的电子健康记录 (EHR) 数据,就像ICD代码一样,往往缺乏准确的疾病特征所需的细节性.
- 电子健康记录数据的局限性阻碍了疾病发病研究和强大的人工智能 (AI) 系统的开发.
- 临床笔记提供了丰富的信息,但由于处理非结构化文本的挑战,这些笔记未得到充分利用.
研究的目的:
- 引入Ci-SSGAN (临床知情半监督生成对抗网络),这是一个新的框架,可以使用未标记的临床文本重新注释患者的病情.
- 提高患者状况数据集的准确性和公平性,用于研究和人工智能开发.
- 解决目前用于研究复杂疾病和差异的电子健康记录数据的局限性.
主要方法:
- 开发了Ci-SSGAN,一个临床知情的半监督生成对抗网络框架.
- 利用大规模的未标记的临床文本数据进行重新注释.
- 将框架应用于眼,它是导致失明的主要原因,具有已知的种族和种族差异.
主要成果:
- 在眼科笔记上,Ci-SSGAN实现了0.85精度和0.95 AUROC,超过了ICD的标签 (0.74精度,0.85 AUROC) 的10.19%AUROC.
- 该框架证明了公平的改善,缩小了黑人患者 (+0.05 F1),女性 (+0.06 F1) 和年轻患者 (+0.033 F1) 的绩效差距.
- 该模型在210万张眼科笔记上受过训练.
结论:
- Ci-SSGAN有效地从临床笔记中重新注释患者的病情,提高准确性和公平性.
- 该框架的半监督学习和人口条件的整合将对专家注释的依赖降到最低.
- Ci-SSGAN为资源有限的医疗保健系统,特别是具有差异的复杂疾病提供了更容易获得的AI开发方法.
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