使用机器学习来改善医疗索赔数据中的过敏反应病例识别
Kamil Can Kural1,2, Ilya Mazo1, Mark Walderhaug1
1Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, Silver Spring, MD 20993, United States.
JAMIA open
|October 30, 2023
概括
机器学习模型可以准确地识别医疗保健数据库中的过敏反应,匹配专家算法. 这种方法可以改善病例的发现,并简化严重过敏反应的算法开发.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 公共卫生数据分析
背景情况:
- 过敏反应是一种危急的,危及生命的过敏反应.
- 在医疗保健数据库中准确识别对于公共卫生和研究至关重要.
- 利用"大数据"需要精确的案例识别方法.
研究的目的:
- 评估机器学习 (ML) 的实用性,以识别索赔数据中的事件过敏症病例.
- 开发和测试使用反映现实世界数据质量的各种数据集的ML模型.
- 识别用于增强现有的过敏反应检测算法的新特性.
主要方法:
- 从2015年10月到2019年2月使用的索赔数据 (CMS数据库).
- 实施了关键数据点的特征选择管道.
- 应用无监督和监督的ML方法 (例如,萨蒙映射,极端梯度增强).
主要成果:
- 机器学习模型的准确性在地面真相数据上从47.7%到94.4%不等.
- 确定了可以帮助专家改进案例查找算法的新功能.
- 经过证明的ML模型的性能与已建立的专家算法相当.
结论:
- 机器学习为医疗条件的传统,劳动密集型算法开发提供了可行的替代方案.
- ML模型可以匹配或超过专家驱动的过敏反应检测算法的性能.
- 通过ML识别新型特征可以增强和简化创建精确的医疗结果检测算法.
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