Process / pipeline
临床文本挖掘 — 临床自然语言处理信息抽取
临床文本挖掘是自然语言处理的一个专门分支，它从非结构化的医疗保健文档（如出院总结、病程记录和放射报告）中提取结构化的临床事实——诊断、症状、药物、治疗和ICD编码。该方法基于BioBERT（Lee et al., 2020）和i2b2/UTHealth共享任务基准（Stubbs & Uzuner, 2015）等生物医学NLP模型，将自由文本的临床叙述转换为机器可读的数据，适用于临床决策支持和健康分析。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240. DOI: 10.1093/bioinformatics/btz682 ↗
- Stubbs, A. & Uzuner, Ö. (2015). Annotating risk factors for heart disease in clinical narratives for the 2014 i2b2/UTHealth shared task. Journal of the American Medical Informatics Association, 22(e1), e30–e39. link ↗
如何引用本页
ScholarGate. (2026, June 1). Clinical Text Mining (Clinical NLP Information Extraction). ScholarGate. https://scholargate.app/zh/text-mining/clinical-text-mining
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →