一种新的中文电子病历文本检索模型

doi:10.11896/jsjkx.210400198

摘要/Abstract

摘要： 电子病历的增长构成用户健康大数据的基础,可提高医疗服务质量并降低医疗成本,因此迅速有效地检索病例在临床医学中具有实际意义。电子病历具有极强的专业性和独特的文本特点,然而传统的文本检索方法存在文本实体语义表达不准确、检索精度较低的不足。针对以上特点及问题,提出一种融合BERT-BiLSTM模型结构,以充分表达电子病历文本语义信息,提高检索的准确率。依据公开数据,首先,将公开的标准中文电子病历数据按临床诊断规则做关联扩展检索主题词预处理;其次,利用BERT模型,根据病历文本的上下文语境动态获取字粒度向量矩阵,再将生成的字向量作为双向长短时记忆网络模型(BiLSTM)的输入,以提取上下文信息的全局语义特征;最后,将检索文档的特征向量映射到欧氏空间中,找出与检索文档距离最近的病历文本,实现非结构化临床数据文本检索。仿真结果表明,该方法能够从病历文本中挖掘出多层次、多角度的文本语义特征,在电子病历数据集上取得的F1值为0.94,能显著提高文本语义检索准确率。

关键词: BERT模型, BiLSTM, 电子病历, 扩展检索主题词, 文本检索

Abstract: The growth of electronic medical records forms the basis of user health big data,which can improve the quality of medi-cal services and reduce medical costs.Therefore,the rapid and effective retrieval of cases has practical significance in clinical medi-cine.Electronic medical records have strong professionalism and unique text characteristics.However,traditional text retrieval methods have the disadvantages of inaccurate text entity semantic expression and low retrieval accuracy.In view of the above characteristics and problems,this paper proposes a fusion BERT-BiLSTM model structure to fully express the semantic information of the electronic medical record text and improve the accuracy of retrieval.This research is based on public data.First,correlation extension retrieval keywords prerpocessing is carried on the open standard Chinese EMR data according to clinical diagnosis rules.Secondly,the BERT model is used to dynamically obtain the word granularity vector matrix according to the context of the medical record text,then the generated word vector is used as the input of the bidirectional long and short-term memory network model(BiLSTM) to extract the global semantic features of the context information.Finally,the feature vector of the retrieved document is mapped to the Euclidean space,and the medical record text closest to the retrieved document is found to realize the text retrieval of unstructured clinical data.Simulation results show that this method can dig out multi-level and multi-angle text semantic features from the medical record text,the F1 value obtained on the electronic medical record data set is 0.94,which can significantly improve the accuracy of text semantic retrieval.

Key words: BERT model, Bidirectional long and short-term memory network model, Electronic medical record, Extended search keywords, Text retrieval

中图分类号:

TP391

于家畦, 康晓东, 白程程, 刘汉卿. 一种新的中文电子病历文本检索模型[J]. 计算机科学, 2022, 49(6A): 32-38. https://doi.org/10.11896/jsjkx.210400198

YU Jia-qi, KANG Xiao-dong, BAI Cheng-cheng, LIU Han-qing. New Text Retrieval Model of Chinese Electronic Medical Records[J]. Computer Science, 2022, 49(6A): 32-38. https://doi.org/10.11896/jsjkx.210400198

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