Research On Computer Aided Diagnosis And Treatment Based On TCM

Chinese medicine is a treasure of the Chinese nation,a unique health resource in China,and plays an important role in China’s social and economic development.With the development of computer and Internet technology,"Internet + medical care" has provided impetus for the development of traditional Chinese medicine.Combined with "Internet +" technology,a TCM auxiliary diagnosis and treatment system is constructed according to the process of the traditional Chinese medicine(TCM)consultation and prescription.This system can help TCM reduce the memory and search of various herbs and prescriptions,improve the medical level and efficiency of TCM,and effectively solve the shortage of TCM resources.TCM diagnosis and treatment consists of two stages: diagnosis and treatment,which are closely related to each other.Firstly,TCM comprehensively understands the patient’s condition through the combination of four diagnosises and consultations,and then makes a diagnosis of the patient according to the symptoms and signs of the patient and his own clinical experience,and finally makes a reasonable treatment plan for the patient on the basis of the diagnosis results.Therefore,auxiliary diagnosis and treatment is a key technical problem in TCM auxiliary diagnosis and treatment system.Aiming at the key technical problems of TCM auxiliary diagnosis and treatment system,this paper proposes an integrated tree of medical auxiliary diagnosis method based on rough set,and Ontology knowledge base based method of drug use and prescription recommendation,and preliminarily constructs a TCM auxiliary diagnosis and treatment platform.The main research contents are:1.The medical auxiliary diagnosis method of integrated tree based on rough set.In order to reduce the redundancy of medical data,extract useful medical value quickly and improve the accuracy of medical diagnosis.Firstly,the attribute reduction algorithm based on rough set information entropy is used for attribute reduction of medical data,and then the Bagging integrated tree algorithm based on C4.5 decision tree is used to train and learn the case set generated by attribute reduction.Finally,the experimental results show that method can not only reduce the redundancy of medical data,but also improve the accuracy of medical diagnosis.2.The drug using and prescription recommendation method based on ontology knowledge base.In order to improve the sharing and reuse of knowledge,the ontology knowledge base of TCM domain is constructed by using the seven-step method,drug recommendation method based on Apriori algorithm and prescription recommendation method based on collaborative filtering were also constructed.Finally,the experiment shows that the drug recommendation method based on Apriori algorithm can effectively recommend Chinese medicine to traditional Chinese medicine,while the ontology-based collaborative filtering prescription recommendation method has high accuracy.3.The construction of TCM auxiliary diagnosis and treatment platform.Using the "TCM syndrome differentiation and treatment system" combined with computer application and information network technology,using the Java programming language to initially build the TCM auxiliary diagnosis and treatment platform,to achieve the auxiliary herbalist doctor disease diagnosis,recommendation of classic prescriptions and prescriptions of famous old Chinese medicine,as well as TCM knowledge retrieval and learning function.In this paper,an integrated tree method based on attribute reduction of rough set entropy is adopted to assist herbalist doctor in disease diagnosis and classification.The method of drug use and prescription recommendation based on ontology knowledge base is adopted to recommend drug use and prescription for herbalist doctor.The preliminary construction of TCM auxiliary diagnosis and treatment platform has realized the diagnosis and treatment of patients by auxiliary herbalist doctor.