Construction Of Tumor Knowledge Graph Based On Chinese Electronic Medical Records

In recent years,the incidence and mortality of malignant tumors have been increasing all over the world.How to use the existing experience of diagnosis and treatment to sum up and explore the potential and effective relationship between diagnosis and treatment,so as to strengthen the prevention and treatment of malignant tumors?It has become an urgent problem for medical workers to solve.With the development of medical and health information in China,major hospitals have accumulated a wealth of EMRs(Electronic Medical Records).EMRs contain rich medical facts,but its unstructured text structure,including a large number of medical terms,acronyms and other characteristics,brings great challenges to the organization and utilization of EMRs under the environment of big data.As an important part of artificial intelligence,knowledge graph has strong ability of information processing and knowledge organization,which provides a new way to solve this problem.In order to meet the needs of tumor knowledge graph construction based on CEMRs(Chinese Electronic Medical Records),this study puts forward a complete framework of tumor knowledge graph based on CEMRs according to the structure and language characteristics of tumor disease and Chinese tumor electronic medical records.It provides ideas for the construction of tumor knowledge graph.Taking digestive system tumor as an example,this study designs and constructs the knowledge graph of digestive system tumor,and evaluates the quality by means of quantitative evaluation and expert evaluation.Specifically,the main work of this study includes the following four parts:(1)Systematically comb the research status of knowledge graph at home and abroad,draw lessons from the existing research ideas and related technologies,and summary the limitations of the existing research,including:?In data sources,less use of clinical data,especially CEMRs;?The current research is mostly focused on the data level,but the research on construction of knowledge graph schema is not enough;?In terms of semantic relationship,the defined semantic relationship is relatively simple and can not accurately express the complex relationship between medical facts in the process of disease diagnosis and treatment;?In natural language processing tools,lack of efficient natural language processing tools for Chinese medical texts.(2)Propose a framework of tumor knowledge graph based on CEMRs.The structure and language characteristics of tumor diseases and CEMRs are analyzed in detail.On the basis of defining the design principles of tumor knowledge graph and defining design ideas,in view of the shortcomings of the existing research,focusing on the lack of research on the construction of tumor knowledge graph schema and the lack of semantic consideration,combined with the characteristics of tumor diseases and CEMRs,a complete framework of tumor knowledge graph construction based on CEMRs is proposed.(3)Construct a knowledge graph of digestive system tumor with rich semantic relations.In order to verify the feasibility and science of the construction framework of tumor knowledge graph based on CEMRs,this study uses the method of empirical research to construct the knowledge graph of digestive system tumor by taking digestive system tumor as an example.First of all,combined with the characteristics of digestive system tumor diseases,such as pathological staging and histological classification of digestive system tumors,using the model construction "seven steps" proposed by Stanford University to construct the knowledge graph schema of digestive system tumor by referring to i2b2 2010,SNOMED CT,NCI thesaurus,ICD-10 and WHO classification of digestive system tumors.The schema includes 7 kinds of entities and 15 kinds of semantic relations.Combined with the characteristics of tumor electronic medical records,such as a large number of idioms,fixed grammar and syntax,and the same type of tumor entity appearance in pairs,this study introduces the concept of entity group.The method based on rule-based and BiLSTM-CRF model is used to named entity recognition,BiGRU-Attention model is used to extract semantic relationships from EMRs of digestive system tumors.Finally,the data alignment is realized by using the strategy of hierarchical and batch entity alignment.The study uses Neo4j graphic database to store and manage data.(4)Carry out the quality evaluation of knowledge graph of digestive system tumor.By means of quantitative evaluation and expert evaluation,the quality of digestive system tumor knowledge graph is evaluated from three aspects:data layer,model layer and application layer.The evaluation results show that the data of digestive system tumor knowledge graph constructed in this study is more comprehensive and reliable,the schema structure of the graph is reasonable,the content of electronic medical record text can be displayed comprehensively and clearly,and it is convenient for users to search semantics.The framework of tumor knowledge graph based on CEMRs is scientific and practical.