甲状腺超声波通过自然语言处理识别电子健康记录的适当性
Cristian Soto Jacome1, Danny Segura Torres1, Jungwei W Fan2
1Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN.
Mayo Clinic proceedings. Digital health
|March 19, 2024
概括
自然语言处理 (NLP) 算法可以准确地确定甲状腺超声波 (TUS) 的适当性,有助于评估甲状腺癌的过度诊断. 这种方法有助于从临床笔记和要求中识别合适的TUS (aTUS) 和不合适的TUS (iTUS).
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 在瘤学瘤学.
背景情况:
- 甲状腺癌的过度诊断是一个越来越令人担忧的问题,这可能是由于过度使用诊断成像技术造成的.
- 甲状腺超声波 (TUS) 经常进行,但它们的适用性并不总是得到充分记录.
- 开发自动化方法来评估TUS的适当性对于解决过度诊断至关重要.
研究的目的:
- 开发和评估一种自然语言处理 (NLP) 算法,用于确定甲状腺超声波 (TUS) 的适当性.
- 创建一个强大的数据集,用于培训和测试关于TUS适当性的NLP模型.
- 为更广泛的医疗保健系统实施设计一个精简的NLP管道.
主要方法:
- 使用628名梅奥诊所患者 (2017-2021) 的临床笔记和元信息开发了一个基于规则的NLP管道.
- 为培训 (n=468) 和测试 (n=160) NLP模型,建立了一个基于共识的基准真相数据集.
- 一个缩写的NLP管道 (aNLP) 仅使用TUS订单征集标签创建.
主要成果:
- 主要NLP管道实现了高性能:测试组的灵敏度为0.96,特异性为0.80,正预测值为0.99.
- 该培训套件表现出色:适合TUS (aTUS) 的灵敏度为0.99,特异性为0.95,正预测值为1.0.
- 简称NLP管道 (aNLP) 显示了类似的性能指标,表明其潜在的更广泛的应用.
结论:
- NLP模型可以使用临床文档和订单要求准确地分类TUS的适当性.
- 这项技术是分析TUS利用模式及其与甲状腺癌过度诊断的联系的关键第一步.
- 开发的NLP工具为医疗保健系统提供了可扩展的解决方案,以审计TUS的适当性并减轻过度诊断.
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