使用喘的操作定义和机器学习方法,提高喘诊断的准确性
Hyonsoo Joo1, Daeun Lee2, Sang Haak Lee3
1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Uijeongbu St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
BMC pulmonary medicine
|June 6, 2023
概括
传统的喘定义不准确地识别了患者. 机器学习显著提高了喘诊断的准确性,为使用医疗保险数据进行研究提供了更好的方法.
科学领域:
- 肺部医学 肺部医学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 国家医疗保险数据分析对于喘研究和政策至关重要.
- 对于喘的传统操作定义在现实世界的临床环境中缺乏准确性.
- 准确的患者鉴定对于可靠的健康研究和基于证据的政策至关重要.
研究的目的:
- 为了验证喘常规操作定义的准确性.
- 使用机器学习开发一种更准确的喘操作定义.
- 为了提高健康主张数据中喘患者识别的准确性.
主要方法:
- 从两个医院 (2017年1月至2018年1月) 提取了使用常规定义的喘患者.
- 随机抽取10%的抽取患者进行病历审查,以验证诊断的准确性.
- 应用机器学习技术,包括XGBoost,以更准确地预测喘.
主要成果:
- 通过常规定义确定了4235名喘患者; 56%是真正的喘病例.
- XGBoost模型实现了87.1%的精度,93.0%的AUC,82.5%的灵敏度和97.9%的特异性.
- 准确的喘诊断的关键预测因素包括ICS/LABA,LAMA和LTRA.
结论:
- 对喘的常规操作定义在现实世界的准确性上是有限的.
- 建立一个标准化,准确的喘操作定义是必要的.
- 机器学习为在索赔数据研究中开发精确的喘定义提供了一个有希望的方法.
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