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临床知情的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, USA.

NPJ digital medicine

|December 26, 2025

概括

Ci-SSGAN是一种新的AI框架,通过使用临床笔记来增强疾病的特征. 这种临床上知情的,半监督的生成对抗网络提高了患者数据的准确性和公平性,特别是对于像玻璃眼这样的疾病.

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
生物医学数据科学生物医学数据科学

背景情况:

结构化的电子健康记录 (EHR) 数据,就像ICD代码一样,由于噪音和不完整性,往往不足以详细描述疾病.
这限制了研究疾病发病,进展和开发强大的人工智能 (AI) 系统的数据集的准确性.
临床笔记提供了丰富的,未充分利用的数据来源,以获得更细致的患者状况信息.

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,旨在使用大规模未标记的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,用于研究和人工智能开发.
在一个涉及眼的案例研究中证明该框架的实用性,这种疾病具有已知的种族和种族差异.

主要方法:

开发了Ci-SSGAN,一个半监督的生成对抗性网络框架,集成临床信息.
在一个大数据集上训练模型,其中包括349587个未标记的眼科笔记和2954个专家注释的笔记.
纳入人口条件和半监督学习,以尽量减少对专家注释的依赖.

主要成果:

在眼病例研究中,Ci-SSGAN取得了高性能,准确度为0.85,AUROC为0.95.
与传统ICD基标签相比,AUROC显著改善10.19% (0.85AUROC与0.74AUROC对比).
人口分组之间的绩效差距缩小,显示黑人患者 (+0.05),女性 (+0.06) 和年轻患者 (+0.033) 的F1得分增长.

结论:

Ci-SSGAN有效地利用未标记的临床文本,以更准确和公平地重新说明患者的病情.
该框架为人工智能开发提供了一个可扩展的解决方案,减少了对广泛专家注释的依赖.
这种方法增强了在医疗保健中开发先进人工智能系统的潜力,特别是在资源有限的环境中,并解决了健康差异.

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