自然语言处理系统的敏感性和特异性,用于识别使用毒品的住院患者
Leah Benrubi1, Taisuke Sato2,3, Leo K Westgard2
1School of Medicine, Tufts University, Boston, MA, USA.
Open forum infectious diseases
|July 17, 2025
概括
自然语言处理 (NLP) 显著改善了医院数据中使用毒品的人 (PWUD) 的识别. 与传统的国际疾病分类 (ICD-10) 代码相比,这项技术提高了准确性和效率,有助于改善护理.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 计算语言学 计算语言学
背景情况:
- 使用毒品的人 (PWUD) 面临危害减轻和物质使用障碍治疗的障碍.
- 准确跟踪PWUD住院情况对于改善护理系统至关重要.
- 目前使用图表审查和国际疾病分类 (ICD) 代码的方法是劳动密集型的,容易产生不准确性.
研究的目的:
- 评估自然语言处理 (NLP) 在提高PWUD住院诊断的准确性方面的有效性.
- 将NLP工具的敏感性和特异性与传统的ICD-10代码进行比较.
主要方法:
- 从2018年到2023年,分析了塔夫茨医疗中心的住院病例情况.
- 开发和应用两个NLP工具 (正则表达式和开放健康NLP工具包) 来识别PWUD.
- 将NLP算法与ICD-10代码进行比较,使用手动查看790次住院病例的图表作为黄金标准.
主要成果:
- 仅ICD-10代码就显示了低灵敏度 (43%) 和高特异性 (99%).
- 整合NLP系统使灵敏度提高到94%,但使特异性降低到46%.
- 一个结合正则表达式和ICD-10代码的实用模型实现了74%的灵敏度和87%的特异性.
结论:
- 自然语言处理 (NLP) 提供了一种功能,成本效益和准确的方法来识别住院PWUD.
- 通过加强数据识别系统,NLP技术可以提高PWUD的医疗保健公平性.
- 建议进一步开发NLP以完善这些识别能力.
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