摘要
基层医疗机构的医生缺少患病儿童疫苗接种风险的判断能力,通过学习高水平医院医生的经验来研发儿童疫苗接种风险预测模型,从而帮助基层医疗机构医生快速筛查高风险患儿,是一种可行的方案.本文提出了一种智能化的基于知识图谱的疫苗接种建议推荐方法.首先,提出了一种基于预训练语言模型的医学命名实体识别方法ELECTRA-BiGRU-CRF,用于门诊电子病历命名实体抽取.其次,设计疫苗接种本体,定义关系及属性,基于Neo4j构建了中文儿童疫苗接种知识图谱.最后,基于构建的中文疫苗接种知识图谱,提出了一种基于预训练语言模型进行显著性类别指导的疫苗接种建议分类推荐方法.实验结果表明,本文研究方法可以为医生提供辅助诊断,对于患病儿童能否接种疫苗提供决策支持.
Primary healthcare providers lack the ability to assess the risk of vaccination for children with certain illnesses.It is a viable solution to developing a risk prediction model for pediatric vaccination,by leveraging the experience of healthcare professionals in tertiary hospitals,to assist primary healthcare providers in swiftly identifying high-risk pediatric patients.This study proposes an intelligent method for vaccine recommendations based on a knowledge graph.Firstly,a method for medical named entity recognition called ELECTRA-BiGRU-CRF,based on pre-trained language models,is proposed for named entity extraction from outpatient electronic medical records.Secondly,a vaccination ontology is designed,with relationships and attributes defined,to construct a Chinese childhood vaccination knowledge graph based on Neo4j.Finally,a method for vaccine recommendations guided by significant categories using pre-trained language models is proposed based on the constructed knowledge graph.Experimental results indicate that the proposed methods can provide diagnostic assistance to physicians and offer support for deciding whether vaccines can be administered to children with certain illnesses.
作者
吴英飞
刘蓉
李明燕
季钗
崔朝健
WU Ying-Fei;LIU Rong;LI Ming-Yan;JI Chai;CUI Zhao-Jian(School of Information Science and Technology,Hangzhou Normal University,Hangzhou 311121,China;Child Healthcare Department,Children’s Hospital of Zhejiang University School of Medicine,Hangzhou 310003,China;National Clinical Research Center for Child Health,Hangzhou 310003,China)
出处
《计算机系统应用》
2024年第10期37-46,共10页
Computer Systems & Applications
基金
浙江省自然科学基金(TGY24H260008)。
关键词
中文电子病历
预训练语言模型
知识图谱
命名实体识别
疫苗接种建议
Chinese electronic medical record
pre-trained language model(PLM)
knowledge graph
named entity recognition(NER)
vaccination recommendation
