使用深度学习和电子健康记录识别ANCA相关的血管炎队列
Liqin Wang1, John Novoa-Laurentiev1, Claire Cook2
1Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School Boston MA USA.
International journal of medical informatics
|January 26, 2025
概括
使用电子健康记录 (EHR) 的深度学习模型可以比传统方法更有效地识别ANCA相关血管炎 (AAV) 病例. 这种先进的方法有助于发现其他可能被基于规则的算法遗漏的AAV诊断.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 罕见疾病的诊断 罕见疾病的诊断
背景情况:
- 与ANCA相关的血管炎 (AAV) 是一种罕见但严重的自身免疫性疾病.
- 目前使用索赔数据识别AAV病例的方法通常是低效的,可能会忽视特定的患者子组.
- 电子健康记录 (EHR) 为改善疾病识别提供了丰富的数据来源.
研究的目的:
- 开发和评估一个深度学习模型,以准确有效地识别ANCA相关血管炎 (AAV) 病例.
- 在EHR数据中,将深度学习算法的性能与传统基于规则的方法进行比较.
- 评估深度学习的潜力,以识别传统方法遗漏的AAV病例.
主要方法:
- 使用了大规模的临床文档库,来自Mass General Brigham (MGB),跨越了四十年.
- 训练和评估各种机器学习和深度学习算法,包括层次注意网络 (HAN),在标记的EHR笔记部分.
- 使用积极预测值 (PPV),灵敏度,AUROC和AUPRC等指标评估模型性能,并在患者级别评估中将HAN与基于规则的算法进行比较.
主要成果:
- 层次关注网络 (HAN) 在三个数据集中表现出卓越的性能,获得了高的AUROC分数 (高达0.991).
- 包括HAN在内的深度学习模型显示出AAV病例识别的高正预测值 (PPV).
- 在2000个病例的队列中,HAN模型实现了0.975的灵敏度,并且比最佳基于规则的算法识别了13%更多的AAV病例.
结论:
- 深度学习模型对于ANCA相关血管炎 (AAV) 诊断的临床笔记部分的分类是有效的.
- 将深度学习应用于EHR数据可以显著改善AAV病例的检测.
- 这项技术有望揭示传统识别方法遗漏的额外诊断.
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