【目的】研究基于中文電子病歷的標注方法,提升臨床文本分析與處理能力,促進臨床知識發現。【方法】提出中文電子病歷標注思路,并構建可視化交互平臺,基于電子病歷文本的字與詞特征,綜合利用自然語言處理和機器學習方法開展臨床命名實體識別實證研究。【結果】獲得700份標注病歷語料,基于Pipeline的標注方法總體F值達0.8772,較基于原始標注病歷數據集的命名實體識別效果提升32.9%。【局限】由于電子病歷包含與隱私相關的敏感信息,本研究基于開放評測數據開展實驗研究,語料庫大小受限。【結論】本研究所提出的中文電子病歷標注方法和所構建的標注平臺適用于臨床文本處理,能夠促進醫學臨床文本資源的知識關聯化。

[Objective] This paper studies the annotation method for Chinese electronic medical records, aiming to improve the processing of massive clinical texts and clinical knowledge discovery. [Methods] First, we proposed annotation method for Chinese e-medical records, and constructed a visual interactive platform. Then, based on the word and phrase features of these records, we identified the medical name entities with natural language processing and machine learning approaches. [Results] A total of 700 annotated records were obtained, and the overall F value of the Pipeline-based annotation method reached 0.8772, which was 32.9% higher than those based on the original medical records. [Limitations] Since the electronic medical record contains sensitive privacy information, this study was conducted with open dataset, and the corpus size was limited. [Conclusions] The Chinese electronic medical record annotation method and platform constructed in this study could effectively process clinical texts, and the association of medical knowledge.