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从患者的临床笔记中提取控制指标:一种自然语言处理方法

Marta Fernandes¹, Aidan Cardall¹, Lidia Mvr Moura¹

¹Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States.

Epilepsy research

|September 14, 2024

概括

自然语言处理 (NLP) 可以有效地从电子健康记录 (EHR) 中提取控制指标. 这种自动化方法通过分析患者发作频率和最后一次发作日期来帮助大规模的研究.

科学领域:

医疗信息学医疗信息学
计算语言学计算语言学
神经学神经学

背景情况:

监测发作控制对于患者的护理至关重要.
从电子健康记录 (EHR) 中手动抽象度指标是耗时的.
需要自动化方法来有效地提取这些重要的患者数据.

研究的目的:

开发和评估一种自然语言处理 (NLP) 模型,用于抽象控制指标.
专门旨在从临床笔记中提取最后一次发作日期和发作频率.
为患者使用EHR数据进行大规模研究.

主要方法:

使用预训练的 RoBERTa 模型与正则表达式相结合,用于数据提取.
开发了算法来分类最后一次发作时间和发作频率.
使用AUROC,AUPRC和MAE评估模型性能,并对置信区间进行启动.

主要成果:

分析了1773名成年患者在5658次诊所访问 (2018年12月至2022年5月) 的数据.
在最后一次发作日期达到4周的绝对误差 (MAE) 中位数.
在发作频率方面,每天达到0.02次的MAE.

结论:

通过NLP方法,可以从电子健康记录中提取控制指标.
这种方法通过自动化数据抽象来促进的大规模研究.
能够更有效地分析患者发作数据,以获得临床见解.

关键词:

电子健康记录是电子健康记录.是一种病.大型语言模型.自然语言处理自然语言处理.现型化 (Phenotyping) 是一种表现方式.扣押控制扣押控制扣押控制

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