应用自然语言处理框架,从多种癌症类型的病理学报告中提取数据
Phillip Park1,2, Yeonho Choi2, Nayoung Han3
1Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea.
Journal of Korean medical science
|March 3, 2026
概括
这项研究表明,自然语言处理 (NLP) 系统,特别是ClinicalBERT,如何自动从病理报告中提取数据. 这提高了对各种癌症类型的临床数据分析的效率和准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 自然语言处理自然语言处理.
背景情况:
- 病理学报告包含有价值的临床和病理学数据,但很难提取用于研究.
- 开发了一种高效的自然语言处理 (NLP) 系统,以自动从半结构化病理报告中提取数据.
- 该系统在集中数据库中简化了临床数据的存储,检索和分析.
研究的目的:
- 开发和评估用于从病理学报告中提取临床数据的自动化系统.
- 为了比较不同深度学习模型对这个NLP任务的性能.
- 为了确定最优的模型,准确和高效的数据提取.
主要方法:
- 深度学习架构的比较分析,包括LSTM,CNN和基于变压器的模型 (BERT,BioBERT,ClinicalBERT).
- 基于从病理学报告中提取变量的准确性和效率来评估模型性能.
- 选择ClinicalBERT作为基准模型是因为它熟练掌握医学术语和语境.
主要成果:
- 临床BERT在分类多种癌症类型的变量方面表现出卓越的表现.
- 在大多数肝癌变量中,获得了高F1分数 (≥0.99).
- 其他癌症的表现也存在变化,其中一些癌症达到完美得分 (F1=1.0),而另一些需要进一步优化 (例如,胃癌的远程转移).
结论:
- NLP系统,特别是ClinicalBERT,可以有效地自动从病理学报告中提取临床数据.
- 这种自动化方法简化了数据处理,提高了提取信息的准确性.
- 开发的系统有望通过高效的数据利用来改善癌症研究.
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