一种以患者为中心的方法来开发和验证一种自然语言处理模型,用于提取患者报告的症状
Satoshi Watabe1, Yuki Yanagisawa1, Kyoko Sayama1
1Division of Drug Informatics, Keio University Faculty of Pharmacy, 1-5-30, Shibakoen, Minato-ku, Tokyo, 105-8512, Japan.
Scientific reports
|July 29, 2025
概括
一个新的自然语言处理 (NLP) 模型有效地从各种叙述中提取患者报告的症状. 这一进步提高了对患者体验的理解,提高了医疗保健质量.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 患者报告的症状对医疗保健质量至关重要,但难以捕捉.
- 现有的NLP模型主要关注临床笔记,而不是患者生成的文本.
- 需要针对患者叙述量身定制的NLP工具.
研究的目的:
- 开发和验证一种新的NLP模型,用于从制药护理记录中提取患者报告的症状.
- 为了评估模型在多样化,患者生成的叙述上的表现.
- 将新模型与临床文本分析的现有工具进行比较.
主要方法:
- 开发了一个基于变压器的命名实体识别 (BERT-CRF) 模型.
- 使用药物护理记录 ("主体"部分) 来自服用抗癌药物的患者.
- 使用两个注释准则创建了基础真相数据.
- 在外部患者生成的博客数据上验证了模型.
主要成果:
- 伯特-CRF模型显著优于现有的日本临床文本工具.
- 在药物护理记录中获得F1评分>0.8.
- 从患者博客中提取了超过98%的身体症状条目,改善了20%.
结论:
- 微调NLP模型与患者特定的叙事数据是必不可少的.
- 开发的模型有效地捕捉了细微的和通俗的症状表达.
- 这种方法增强了患者生成的健康数据的分析.
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