The Intelligent Diagnostic System for Common Diseases using the Optimized Medical Knowledge Graph

With the development of artificial intelligence technology, Clinical Decision Support System (CDSS) has been widely used. This paper presents a CDSS system, which called the Intelligent Auxiliary Diagnostic System for Common Disease (CD-IADS). CD-IADS assists the primary doctors in the diagnosis of common diseases with the patient's symptoms, signs, tests and personal background, which improves the primary doctors' working efficiency and reduces their misdiagnosis rate. In CD-IADS, medical experts construct the Medical Knowledge Graph according to medical literature and clinical experience firstly. Then, information entropy and particle swarm optimization (PSO) algorithms are used to optimize the special weights based on medical records. The system combines the advantages of the medical expert system and the artificial intelligence algorithm to support the diagnosis of common diseases. The paper takes the common diseases of the respiratory department as an example, and the TOP-1 and TOP-3 diagnostic accuracy rates are up to 64.6% and 86.0% respectively.