结合规则学习与深度学习的诊疗关系抽取  

EXTARCTION OF DIAGNOSIS AND TREATMENT RELATIONSHIP BASED ON RULE LEARNING AND DEEP LEARNING

作　　者：高峰 杨佳欣[1,2,3,4] 顾进广 Gao Feng;Yang Jiaxin;Gu Jinguang(School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,Hubei,China;Big Data Science and Engineering Research Institute,Wuhan University of Science and Technology,Wuhan 430065,Hubei,China;Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial,Wuhan 430065,Hubei,China;Key Laboratory of Content Organization and Knowledge Service of Rich Media Digital Publishing,Wuhan 430065,Hubei,China)

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430065 [2]武汉科技大学大数据科学与工程研究院,湖北武汉430065 [3]湖北省智能信息处理与实时工业系统重点实验室,湖北武汉430065 [4]富媒体数字出版内容组织与知识服务重点实验室,湖北武汉430065

出　　处：《计算机应用与软件》2024年第3期56-62,93,共8页Computer Applications and Software

基　　金：国家自然科学基金项目(U1836118);湖北省自然科学基金项目(2018CFB194);富媒体数字出版内容组织与知识服务重点实验室开放基金项目(ZD2020/09-01)。

摘　　要：诊疗关系的自动识别和抽取有助于医生进行诊疗决策。传统的关系抽取模型对部分数据没有良好的解释性,因此,以神经网络进行规则学习和泛化,设计打分机制,通过规则匹配实现关系抽取,而后对未正确匹配数据进行针对性深度学习模型训练,完成最终的诊疗关系抽取。使用以疾病为中心的诊疗流程相关文本展开实验验证该方法的效果。实验结果表明,该方法不仅通过少量人工规则使关系抽取增加了可解释性,还可以显著提高关系抽取的效果。The automatic identification and extraction of diagnosis and treatment relationships helps doctors make diagnosis and treatment decisions.The traditional relationship extraction model does not have good interpretability for part of the data.Therefore,this paper used neural network for rule learning and generalization,designed a scoring mechanism,and achieved relationship extraction through rule matching.A targeted deep learning model for incorrectly matched data training was proceeded to complete the final diagnosis and treatment relationship extraction.The relevant texts of the disease-centric diagnosis and treatment process were used for experiments to verify the effect of this method.The results show that the text method not only increases the interpretability of relationship extraction through a few manual rules,but also significantly improves the effect of relationship extraction.

关 键 词：人工智能 医疗领域 关系抽取 深度学习 规则学习 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]