| 中文电子病历命名实体识别方法研究 |
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| 引用本文： | 马欢欢,孔繁之,高建强. 中文电子病历命名实体识别方法研究[J]. 医学信息学杂志, 2020, 41(4): 24-29 |
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| 作者姓名： | 马欢欢 孔繁之 高建强 |
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| 作者单位： | 曲阜师范大学软件学院 曲阜 273100,济宁医学院医学信息工程学院 日照 276826,济宁医学院医学信息工程学院 日照 276826 |
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| 基金项目： | 教育部产学合作协同育人项目“高精度人脸识别技术与教学平台建设研究”（项目编号：201801245011）。 |
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| 摘 要： | 针对中文电子病历命名实体识别任务中存在的边界划分不准确、实体识别率不高等问题,提出基于深度学习的CNN-BiLSTM-CRF模型,详细阐述模型结构与原理,采集3 127份中文电子病历数据进行实验以验证模型性能,结果表明该模型具有较好的识别效果及性能。
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| 关 键 词： | 中文电子病历 命名实体识别 卷积神经网络 |
| 收稿时间： | 2019-09-24 |
Study on Named Entity Recognition Method of Chinese Electronic Medical Records |
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| MA Huanhuan,KONG Fanzhi and GAO Jianqiang. Study on Named Entity Recognition Method of Chinese Electronic Medical Records[J]. Journal of Medical Informatics, 2020, 41(4): 24-29 |
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| Authors: | MA Huanhuan KONG Fanzhi GAO Jianqiang |
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| Affiliation: | School of Software, Qufu Normal University, Qufu 273100, China,School of Medical Information Engineering, Jining Medical University, Rizhao 276826, China and School of Medical Information Engineering, Jining Medical University, Rizhao 276826, China |
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| Abstract: | Aiming at the problems of inaccurate boundary division and low entity recognition rate in the Named Entity Recognition (NER) task of Chinese Electronic Medical Records (EMR), the paper proposes a CNN-BiLSTM-CRF model based on deep learning, expounds the structure and principle of the model in detail, and collects 3 127 Chinese EMR for experiments to verify the performance of the model. The results show that this model achieves better recognition effect and better performance. |
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| Keywords: | Chinese Electronic Medical Records(EMR) Named Entity Recognition(NER) Convolutional Neural Network(CNN) |
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