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基于RoBERTa-CRF的肝癌电子病历实体识别研究
引用本文:邓嘉乐,胡振生,连万民,华赟鹏,周毅.基于RoBERTa-CRF的肝癌电子病历实体识别研究[J].医学信息学杂志,2023,44(6):42-47.
作者姓名:邓嘉乐  胡振生  连万民  华赟鹏  周毅
作者单位:1. 中山大学中山医学院;2. 广东省第二人民医院;3. 中山大学附属第一医院
基金项目:国家重点研发计划(项目编号:2021YFC2009402);国家重点研发计划(项目编号:2022YFC3601600);广东省自然科学基金项目(项目编号:2021A1515011897)。
摘    要:目的/意义 肝癌电子病历中蕴涵大量医学专业知识,且大部分以非结构化数据形式存在,难以自动化提取。肝癌电子病历实体识别研究有助于构建肝癌领域医疗辅助决策系统和医学知识图谱。方法/过程 构建RoBERTa算法与CRF算法相结合的命名实体识别模型,利用自标注肝癌电子病历真实数据进行模型训练与测试。结果/结论 RoBERTa-CRF模型优于其他基线模型,具有较好实体识别效果。

关 键 词:肝癌电子病历  实体识别  知识提取  RoBERTa-CRF模型  自然语言处理
修稿时间:2023/3/18 0:00:00

Study on Entity Recognition of Liver Cancer Electronic Medical Records Based on RoBERTa-CRF
DENG Jiale,HU Zhensheng,LIAN Wanmin,HUA Yunpeng,ZHOU Yi.Study on Entity Recognition of Liver Cancer Electronic Medical Records Based on RoBERTa-CRF[J].Journal of Medical Informatics,2023,44(6):42-47.
Authors:DENG Jiale  HU Zhensheng  LIAN Wanmin  HUA Yunpeng  ZHOU Yi
Institution:Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China;Guangdong Second Provincial General Hospital, Guangzhou 510317, China;The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
Abstract:Purpose/Significance Electronic medical records (EMR) of liver cancer contain a large amount of medical knowledge, and most of the knowledge is in the form of unstructured data which is difficult to extract automatically. The research on entity recognition of liver cancer EMR is important in the construction of clinical decision support systems and medical knowledge graphs in the area of liver cancer. Method/Process A named entity recognition (NER) model combined with RoBERTa algorithm and CRF algorithm is built, and the model achieves excellent effect. The real data of self-labeled EMR of liver cancer are used for model training and testing. Result/Conclusion RoBERTa-CRF model is better than other baseline models and has good entity recognition effect.
Keywords:electronic medical records of liver cancer  entity recognition  knowledge extraction  RoBERTa-CRF model  natual language processing(NLP)
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