摘要
【目的/意义】通过知识图谱实现突发公共卫生事件医疗知识表示与推理,以驱动辅助诊疗应用,促进医疗知识的有效融合和充分利用。【方法/过程】以新冠肺炎疫情为例,首先采用BiLSTM-CRF和TF-IDF识别病例信息中的症状词汇,通过哈工大同义词林扩充症状词,并运用词汇相似度从在线本体提取可重用的概念及层次关系。然后基于RDFlib本体自动化策略构建医疗知识图谱。以最新版的诊疗方案为依据,设计SWRL规则,通过推理规则和neo4j进行病例分型、医疗方案生成和诊疗知识的可视化。【结果/结论】基于68条SWRL推理规则推断出6类人员的类型和基于13类主要治疗方案的中西医组合治疗措施,也实现了症状、诊疗方案等数据和知识的融合。【创新/局限】融合实际病例症状和在线本体知识,将诊疗方案作为知识图谱推理规则的依据,从数智化角度实现医疗知识表示和推理。仍需进一步扩充病例特征与实际的治疗措施,为智能辅助诊疗提供更丰富的依据。
【Purpose/significance】Knowledge representation and reasoning for treatment of public health emergencies through a knowledge graph to drive assisted treatment applications and promote the effective integration and utilization of medical knowledge.【Method/process】Taking the COVID-19 epidemic as an example,firstly,BiLSTM-CRF and TF-IDF are used to identify the symptom vocabulary in the case information,expand the symptom words by HIT synonym forest,and extract reusable concepts and hierarchical relationships from online ontologies using lexical similarity.Then medical knowledge graphs are constructed based on RDFlib ontology automation strategy.Based on the latest version of the treatment regimen,SWRL rules are designed to perform case typing,medical plan generation and visualization of treatment knowledge by inference rules and neo4j.【Result/conclusion】Based on 68 SWRL inference rules,6 types of persons and a combination of Chinese and Western medical treatment measures based on 13 main types of treatment plans were inferred,the integration of data and knowledge of symptoms and treatment regimen are also achieved.【Innovation/limitation】The actual case symptoms and online ontology knowledge are fused,and the treatment regimen is used as the basis for the inference rules of the knowledge graph to realize medical knowledge representation and inference from a digital intelligence perspective.Further expansion of case characteristics and actual treatment measures is still needed to provide a richer basis for intelligent assisted diagnosis and treatment.
作者
王成文
熊励
WANG Chengwen;XIONG Li(School of Management,Shanghai University,Shanghai 200444,China)
出处
《情报科学》
CSSCI
北大核心
2023年第4期164-174,共11页
Information Science
基金
国家社会科学基金重大项目“基于新一代信息技术提高应对突发公共卫生事件能力研究”(21ZDA105)。
作者简介
王成文(1986-),男,河南信阳人,博士研究生,主要从事知识图谱与智能决策研究;通讯作者:熊励(1966-),女,湖北武汉人,博士,教授,博士生导师,主要从事突发公共卫生事件应急决策与知识管理研究,xiongli8@163.com。
