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
1Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States.
medRxiv : the preprint server for health sciences
|September 26, 2025
概括
Ci-SSGAN是一种新的AI框架,使用临床笔记增强了疾病特征,提高了患者数据的准确性和公平性. 这种方法比传统方法提供了更好的性能,特别是对于代表性不足的群体.
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
- 医疗数据分析 医学数据分析
背景情况:
- 结构化的电子健康记录 (EHR) 数据,就像ICD代码一样,往往不足以进行详细的疾病特征.
- 电子健康记录数据的局限性阻碍了准确的疾病致病学研究和AI系统开发.
- 临床笔记提供了丰富的信息,但在很大程度上未得到充分利用.
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,可以使用临床文本重新注释患者的病情.
- 提高患者数据的准确性和公平性,用于疾病研究和人工智能开发.
- 为了解决目前用于复杂疾病表征的电子健康记录数据的局限性.
主要方法:
- 利用大型未标记的临床文本进行半监督学习.
- 使用基于临床数据的生成对抗网络 (GAN) 架构.
- 整合人口条件,以获得公平的AI模型性能.
- 使用210万张眼科笔记,对玻璃眼的病例研究应用.
主要成果:
- Ci-SSGAN实现了0.85准确度和0.95AUROC,用于绿眼病的注释.
- 与基于ICD的标签 (0.85对0.74) 相比,表现出10.19%的AUROC改善.
- 在子组中缩小了表现差距,显示黑人患者 (+0.05),女性 (+0.06) 和年轻患者 (+0.033) 的F1收益.
结论:
- Ci-SSGAN有效地从临床文本中重新注释患者的病情,准确性和公平性很高.
- 该框架显著优于传统的ICD基于标签的复杂疾病,如玻璃眼.
- Ci-SSGAN的半监督方法和人口条件降低了对专家注释的依赖,提高了医疗保健中的AI可访问性.
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