Ontology-Based Personalized Recommendation Research Of Disease Case

With the development of medical informatization,information technology has been widely applied to various medical process.To a certain extent,managing medical processes through medical information systems improve the quality and efficiency of medical services.However,these medical information have not been effectively used.The operations on these information are mainly the functions of adding,deleting,checking and modifying to realize information recording.In order to enable users to easily obtain medical information,a large number of medical information websites have appeared on the Web.These websites mainly provide keyword search services for information on diseases,symptoms,drugs,medical records and so on.This inquiry service enables ordinary users to obtain some medical related information from the website.Nonetheless,due to the inherent limitations of keyword-based query services,it often appear to be inaccessible,inaccurate and incomplete.As a record carrier for medical staff's medical activities,Medical record contains a wealth of medical knowledge.Users expect to quickly and effectively obtain medical records,and then learn more reliable medical knowledge in related medical records.Keyword-based medical records query service cannot meet the needs of users.Ontology is a hotspot branch in the field of artificial intelligence.It is widely used in the fields of knowledge engineering,semantic web and semantic retrieval.Ontology describe the relationship between concepts at the level of semantics and knowledge,which helps computers understand the semantic information contained in concepts.The main work of this paper is as follows:(1)Analyze the existing Web medical resources and ontology construction methods,propose a semi-automatic ontology construction method and build an abundant medical domain ontology.After researching the existing ontology-based semantic relevance algorithm,a concept semantic relevance algorithm and a text correlation calculation method are proposed.The experimental results are in line with expectations.(2)An ontology-based query expansion method was proposed to more accurately capture the users' s query intent.(3)Disease case recommendation algorithms based on disease case relevance and user relevance were proposed to provide users with a new way of acquiring medical knowledge.(4)Designing experiments to evaluate the performance of personalized disease case recommendation algorithms by examining recall,accuracy and F-values.Experiments show that these algorithms have better performance and have certain application value.