准确的临床实体识别和解剖病理学报告的代码映射使用生物临床BERT通过检索增强生成增强:混合深度学习方法
Hamida Abdaoui1, Chamseddine Barki1, Ismail Dergaa2
1Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis (ISTMT), University of Tunis El Manar, Tunis 1006, Tunisia.
Bioengineering (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种自动化管道,用于从解剖病理学报告中提取和标准化信息,从而提高了研究数据的可访问性. 该系统在实体识别和多实体规范化方面表现出高精度,克服了手动数据处理的局限性.
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.
- 计算病理学计算病理学
背景情况:
- 解剖病理学报告是无结构的,阻碍了自动数据提取和大规模研究.
- 手动数据标准化是资源密集型的,难以扩展.
研究的目的:
- 开发和评估一个自动化管道,用于实体提取和多本体论规范化解剖病学报告.
- 增强数据互操作性,促进使用临床文本的自动化研究.
主要方法:
- 根据样本类型,进行的测试和发现,对560份报告进行了注释.
- 实体提取使用了BioBERT v1.1.1.
- 在正常化中使用了BioClinicalBERT与检索增强生成,而不是SNOMED CT,LOINC和ICD-11.
主要成果:
- 获得了高提取性能 (整体F1得分为0.963).
- 结合的BioClinicalBERT和密集检索方法在术语映射方面表现优于其他方法.
- 实质性到近乎完美的协议 (科恩的卡帕0.7829-0.9773) 证明了强大的编码.
结论:
- 开发的管道为解剖病理实体提供了强大的自动提取和多本体学编码.
- 基于变压器的命名实体识别和检索增强生成支持系统的功能.
- 由于研究的局限性,建议在临床部署之前进行多机构验证.
关键词:
生物临床BERT是什么在ICD-11中,它被列为ICD-11.在LOINC中,您可以使用LOINC.这就是SNOMED CT.解剖病理学报告代码映射的代码映射是什么深度学习是一种深度学习.命名实体的认可 命名实体的认可自然语言处理自然语言处理.变压器架构的架构更多相关视频
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