Design And Development Of A Semantic-based Chronic Disease Self-Management System

With the development of industrialization and urbanization, the accelerated process of aging population and the change of lifestyle, there is a gradually increase in the prevalence of chronic disease. Treatment and care of the growing group of patients with chronic diseases lead to great economic burden and health service challenges. Strengthen health education for patients with chronic diseases and improve the patient’s self-management abilities, can effectively control chronic disease and delay the occurrence of complications, thus reducing the financial burden of patients, improving the input-output ratio of healthcare and changing the present condition of chronic disease prevention.In addition to patients’understand of their disease status, chronic diseases self-management requires the patient to master many aspects of knowledge and skills, such as diet, exercise, medication, monitoring of vital signs and so on. Because of the powerful abilities of knowledge representation and reasoning, semantic web technologies were utilized in this paper to model the process of chronic diseases self-management. Based on the semantic model, personalized treatment plans and services were achieved for the patients with chronic diseases, which improved the operability and effectiveness of chronic disease self-management implementation.In this paper, according to the actual needs of chronic disease self-management, design and build the self-management of chronic diseases semantic model, and model-based design and implement a mobile chronic disease self-management system. According to the actual needs of chronic disease self-management, a chronic diseases database was designed and constructed in this paper. Based on this database, a mobile chronic disease self-management system was implemented. Integrating the domain knowledge of chronic disease self-management and patients’semantic clinical data, the system created individualized treatment plans and recommended a personalized implementation program for patients. Through the analysis of the semantic process data produced during the patients’treatment, the system searched the reasons of patients’non-compliance or poor compliance, and proposed appropriate solutions to improve patients’treatment compliance. The system guided patients with chronic disease and helped patients overcome the difficulties encountered in the process of self-management, which encouraged patients to insist on chronic disease self-management.