Intelligent Management Of Clinical Pathway Based On Swarm Intelligence Algorithm And Fuzzy Neural Network

With the increase in the income of residents and the advancement in medical technology,the medical expenses have risen sharply,and the quality of medical care has not been greatly improved.Excessive medical conditions are widespread.The clinical pathway of the standardized diagnosis and treatment process has emerged.However,the papery clinical pathway contains limited information,and static texts cannot reflect the dynamically changing medical process.Therefore,informatization and intelligence have become inevitable trends in clinical pathway research.This article first abstracts the concept and structure of clinical pathways,builds an ontologybased clinical pathway model,and adopts intelligent technologies to solve two key issues of clinical pathway optimization scheduling and mutation processing.This article abstracts the public concept and structure of the clinical pathway,builds a flexible and expandable clinical pathway model based on ontology technology,and uses the OWL language to formally define the model.Secondly,the clinical pathway optimization scheduling is abstracted as a Resource constrained multiproject scheduling problem,a corresponding mathematical model is established,a hybrid particle swarm gray wolf optimization algorithm is proposed,and a optimized clinical pathway scheduling scheme can be solved.Finally,aiming at the complex and nonlinear characteristics of clinical pathway variances processing,a self-organizing fuzzy neural network based on improved k-means and Levenberg-Marquardt algorithm is proposed to take the mutation treatment in the clinical pathway of type I diabetes as an example,and verify the accuracy and generalization of the model through experiments.Intelligent management provides a guarantee for the stable and efficient execution of the clinical pathway,and fully utilizes the clinical pathway to reduce medical costs and improve the quality of medical indicators.