电子健康记录的NLP驱动分析用于早期识别结核病例
Araddhana Arvind Deshmukh1, Abhijit Chitre2, Vina M Lomte3
1Department of Computer Science & Information Technology (Cyber Security), Symbiosis Skill and Professional University, Pune, Maharashtra, India.
The Indian journal of tuberculosis
|December 16, 2025
概括
本研究介绍了一种NLP框架,使用电子健康记录来更快地检测结核病 (TB). 该系统实现了95.1%的准确性,显著改善了早期风险分层,减少了诊断延迟.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 公共卫生 公共卫生
背景情况:
- 结核病 (TB) 仍然是一个重大的全球卫生挑战,因诊断延迟而加剧.
- 目前的诊断方法,如放射学和唾液显微镜,在灵敏度,可扩展性和资源可访问性方面存在局限性.
研究的目的:
- 开发和评估自然语言处理 (NLP) 框架,以使用电子健康记录 (EHR) 早期和准确地检测结核病.
- 通过在EHR中整合结构化和非结构化数据来提高预测准确度.
主要方法:
- 采用了预处理管道,包括使用UMLS和SNOMED CT. de-identification,文本规范化和医疗术语标准化.
- 研究了诸如 Bag-of-Words,TF-IDF,Word2Vec,GloVe 和 BioBERT 嵌入式等特征提取方法.
- 基于变压器的模型,特别是BioBERT,用于分析EHR数据中的语言模式.
主要成果:
- 通过使用BioBERT上下文嵌入,NLP框架实现了95.1%的高精度和94.6%的灵敏度.
- 整合系统化的EHR元素和上下文嵌入,大大减少了虚假负面.
- 早期结核病风险分层变得更加可信,有助于临床决策.
结论:
- 拟议的NLP驱动框架显示了改善临床环境中的结核病早期诊断的巨大潜力.
- 利用EHR数据的NLP提供了一种可扩展和高效的方法来打击全球结核病流行.
- 该框架通过提供更准确和及时的结核病风险评估来增强临床决策支持系统.
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