从电子健康记录中通过自然语言处理从类风湿性关节炎相关的间歇性肺病中提取强制生命能力
Bryant R England1, Punyasha Roul1, Yangyuna Yang1
1VA Nebraska-Western Iowa Health Care System & Division of Rheumatology & Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Pharmacoepidemiology and drug safety
|December 19, 2023
概括
一个新的自然语言处理 (NLP) 工具从类风湿性关节炎-间歇性肺病 (RA-ILD) 患者的电子健康记录 (EHR) 中提取强迫生命能力 (FVC) 值. 与肺功能测试 (PFT) 设备相比,这种NLP方法将准确的纵向FVC数据的捕获量增加了三倍.
科学领域:
- 医疗信息学 医疗信息学
- 肺部病理学 肺部病理学
- 自然语言处理自然语言处理.
背景情况:
- 类风湿性关节炎 - 间歇性肺病 (RA-ILD) 需要精确监测肺功能.
- 强迫性生命能力 (FVC) 是RA-ILD的疾病严重程度的关键指标.
- 从电子健康记录 (EHR) 中提取纵向FVC数据存在挑战.
研究的目的:
- 开发和验证一种自然语言处理 (NLP) 工具,用于从EHR笔记中提取FVC值.
- 为了改善RA-ILD患者的纵向FVC数据的捕获.
主要方法:
- 从退伍军人卫生管理局 (VA) 数据库 (2000-2020) 选择了7485名RA-ILD患者.
- 开发了NLP以从临床笔记中识别和提取FVC值,计算数据变化和分配准确的日期.
- 将NLP衍生FVC值与患者子集肺功能测试 (PFT) 设备数据进行比较.
主要成果:
- NLP从4982名患者中提取了15383个FVC值,远远超过PFT设备 (1,844名患者的5911个值).
- 与日期匹配的NLP和PFT FVC值显示出高度一致 (95.8%在5%内预测,r=0.94,p<0.001).
- 在NLP中,使用纵向FVC数据捕获的患者数量是NLP的三倍,从而实现了更全面的疾病跟踪.
结论:
- 电子健康记录的NLP显著提高了准确的,纵向的FVC值的提取.
- 与传统的PFT设备相比,这种NLP工具的数据捕获率增加了三倍.
- 该工具可以通过提供关键疾病严重程度测量来促进RA-ILD和其他肺部疾病的关键药物流行病学研究.
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