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, Massachusetts, USA.
medRxiv : the preprint server for health sciences
|July 1, 2024
概括
深度学习模型从电子健康记录 (EHR) 中准确识别ANCA相关血管炎 (AAV) 病例,优于传统方法. 这种AI方法可以发现基于规则的算法错过的额外的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
- 临床数据分析 临床数据分析
背景情况:
- 与ANCA相关的血管炎 (AAV) 是一种罕见的,严重的疾病.
- 使用索赔数据进行传统病例识别是耗时的,可能会错过患者子组.
- 电子健康记录 (EHR) 为准确的病例识别提供了一个潜在的替代方案.
研究的目的:
- 开发和评估一个深度学习模型,用于使用EHR数据识别AAV病例.
- 将深度学习模型的性能与传统基于规则的算法进行比较.
主要方法:
- 使用了大量的MGB临床文档 (1979-2021) 存储库.
- 创建了三个标记的数据集,用于培训和评估的笔记部分.
- 训练和评估各种机器学习和深度学习算法用于笔记级分类.
- 对基于规则的算法对深度学习模型的患者级分类进行了评估.
主要成果:
- 深度学习模型在数据集中获得了高的AUROC得分 (0.983-0.991) 和PPV (0.800-0.954).
- 在2000个病例的队列中,深度学习模型实现了0.262的PPV和0.975.97的灵敏度.
- 深度学习模型比最好的基于规则的算法发现了13%的AAV病例.
结论:
- 深度学习模型有效地分类AAV诊断的临床笔记.
- 将深度学习应用于EHR数据可以识别传统方法遗漏的AAV病例.
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