运用自然语言处理方法从中文手术记录中提取重要信息。
Extracting important information from Chinese Operation Notes with natural language processing methods.
机构信息
Shanghai Public Health Clinical Center, Institutes of Biomedical Sciences, and Key laboratory of Medical Molecular Virology, Ministry of Education and Health, Fudan University, Shanghai, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Fudan University, Shanghai, China; Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Zhongshan Hospital, Fudan University, Shanghai, China.
出版信息
J Biomed Inform. 2014 Apr;48:130-6. doi: 10.1016/j.jbi.2013.12.017. Epub 2014 Jan 31.
Extracting information from unstructured clinical narratives is valuable for many clinical applications. Although natural Language Processing (NLP) methods have been profoundly studied in electronic medical records (EMR), few studies have explored NLP in extracting information from Chinese clinical narratives. In this study, we report the development and evaluation of extracting tumor-related information from operation notes of hepatic carcinomas which were written in Chinese. Using 86 operation notes manually annotated by physicians as the training set, we explored both rule-based and supervised machine-learning approaches. Evaluating on unseen 29 operation notes, our best approach yielded 69.6% in precision, 58.3% in recall and 63.5% F-score.
从非结构化的临床叙述中提取信息对于许多临床应用都很有价值。尽管自然语言处理 (NLP) 方法在电子病历 (EMR) 中得到了深入研究，但很少有研究探索 NLP 从中文临床叙述中提取信息。在这项研究中，我们报告了从中文肝癌手术记录中提取肿瘤相关信息的开发和评估。使用 86 份由医生手动标注的手术记录作为训练集，我们探索了基于规则和监督机器学习的方法。在对 29 份未见过的手术记录进行评估时，我们最好的方法在精度、召回率和 F1 分数方面分别达到了 69.6%、58.3%和 63.5%。
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