自然语言处理方法的评估,以识别诊断错误和安全学习系统案例审查数据的分析:回顾性队列研究
Azade Tabaie1,2, Alberta Tran3, Tony Calabria3
1Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, Washington, DC, United States.
Journal of medical Internet research
|August 26, 2024
概括
机器学习和自然语言处理可以通过识别临床笔记中的潜在诊断错误来改善诊断安全监测. 这种方法有助于减少医院手动病例审查的负担.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的机器学习
背景情况:
- 诊断错误导致可预防的死亡率和医院患者的伤害.
- 目前用于识别诊断错误的方法不足,导致逗留时间增加.
研究的目的:
- 评估机器学习 (ML) 和自然语言处理 (NLP) 以改善诊断安全监督.
- 评估使用电子健康记录 (EHR) 临床笔记和病例审查数据的可行性.
主要方法:
- 安全学习系统的分析从一个大型卫生系统 (2016-2021) 的案例审查数据.
- 提取和分析EHR临床笔记.
- 训练后勤回归模型 (包括Ridge,LASSO,弹性网) 来分类诊断错误.
- 性别之间的诊断错误率的统计比较.
主要成果:
- 在1704名患者中,有126名 (7.4%) 患者出现了诊断错误.
- 里奇模型在识别有诊断错误风险的患者方面表现高 (AUC 0.885).
- 在诊断错误的男性和女性患者之间观察到显著的人口和入院差异.
结论:
- NLP技术显示出有效识别诊断错误病例进行审查的潜力.
- 这种方法可以显著减少医疗机构手动案例审查负担.
关键词:
欧洲人权理事会 欧洲人权理事会在NLP中,我们使用了NLP.诊断 诊断 诊断 的 诊断 诊断 诊断 诊断 的 诊断诊断错误 诊断错误 诊断错误 是一个错误.电子健康记录是电子健康记录.医院 医院 医院 医院停留时间的长度.机器学习是机器学习.死亡率 死亡率自然语言处理自然语言处理.伤害患者伤害患者危险的风险 危险的风险更多相关视频
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