基于深度学习的医疗命名实体识别的研究-国际医学与数据杂志-CSCIED科技核心评价数据库-手机版

基于深度学习的医疗命名实体识别的研究

A study on medical named entity recognition based on deep learning

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DOI	10.12208/j.ijmd.20230095
刊名	国际医学与数据杂志 International Journal of Medicine and Data
年，卷(期)	2023, 7(6)
作者	莫大利*,吴冠锋,徐馨怡,
作者单位	贵州医科大学贵州贵阳 ;
摘要	电子病历包含患者的病史、检查结果、诊断和治疗方案等重要医学信息，是支撑医疗智能化研究不可或缺的数据来源。然而由于电子病历的语言表达多样且复杂，医务人员往往难以快速准确地进行信息抽取和分析。基于深度学习的命名实体识别已成为电子病历自动化信息抽取的核心技术。但是医学自然语言处理的复杂性和医疗实体独特性导致电子病历的命名实体识别仍然存在挑战。本文通过构建BERT-MNER模型训练数据集和利用BERT预训练语言模型获取每个字的上下文相关嵌入表示，结合BiLSTM和CRF层完成医疗实体识别。实验结果表明，相较于基于单一深度学习网络的命名实体模型，BERT-MNER模型能更有效地识别电子病历中的医疗实体。
Abstract	Electronic medical record contains important medical information such as patients medical history, examination results, diagnosis and treatment plans, and is an indispensable data source to support intelligent medical research. However, due to the diverse and complex linguistic representations of electronic medical records, it is often difficult for medical professionals to extract and analyze information quickly and accurately. Named entity recognition based on deep learning has become a core technology for automated information extraction from electronic medical records. However, the complexity of medical natural language processing and the uniqueness of medical entities lead to the challenges of named entity recognition in electronic medical records. In this paper, medical entity recognition is accomplished by constructing a BERT-MNER model training dataset and using a BERT pre-trained language model to obtain contextually relevant embedding representations of each word, combined with BiLSTM and CRF layers. The experimental results show that the BERT-MNER model can identify medical entities in electronic medical records more effectively than the named entity model based on a single deep learning network.
关键词	电子病历；BERT-MNER模型；深度学习；命名实体识别
KeyWord	Electronic medical record; BERT--MNER model; Deep learning; Named entity recognition
基金项目
页码	9-13

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引用本文

莫大利*,吴冠锋,徐馨怡. 基于深度学习的医疗命名实体识别的研究 [J]. 国际医学与数据杂志. 2023; 7; (6). 9 - 13.

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