从非结构化的电子健康记录中自动提取中风后的功能结果
Marta Fernandes1, Kaileigh Gallagher2, Niels Turley2
1Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA.
European stroke journal
|January 22, 2025
概括
一个新的自然语言处理 (NLP) 模型准确地从电子健康记录中确定中风后的功能结果 (修改的兰金表). 这种工具有助于大规模的中风研究和人口健康管理.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 精确跟踪中风后的功能结果对于有效的干预和减少残疾至关重要.
- 功能性结果数据往往不完整或埋在非结构化的临床笔记中.
- 自动提取这些数据对于人口层面分析至关重要.
研究的目的:
- 开发和验证一种自然语言处理 (NLP) 模型,用于从电子健康记录 (EHR) 中自动确定修改的兰金表 (mRS).
- 为研究和公共卫生倡议提供中风功能结果的大规模表型化.
主要方法:
- 训练后勤和线性回归模型,使用来自EHR笔记,患者人口统计和出院状态的文本特征.
- 利用现有的mRS分数从注册表作为模型培训和验证的黄金标准.
- 在脑损伤患者的单独数据集上对NLP模型进行外部验证.
主要成果:
- NLP模型实现了高性能,AUROC为0.94,用于预测好与差的mRS,RMSE为0.91,用于测试组中的全部mRS范围.
- 对外部数据集的验证证实了该模型的稳定性,实现了AUROC为0.94和RMSE为1.17.
- 这些模型对住院和出院后的功能结果都表现出了很强的预测准确性.
结论:
- 开发的NLP模型是一个可靠的工具,可以自动从EHR笔记中提取mRS分数.
- 这项技术促进了中风患者的高效,准确的大规模表型化.
- 该模型支持人口健康研究和针对性中风干预措施的开发.
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