Design and Development of a Readmission Risk Assessment System for Patients with Cardiovascular Disease

Recently, cardiovascular disease has become the first cause of death for human health. Because of the complex etiology and long duration, the readmission rate for patients is extremely high during therapy, consuming a large amount of medical resources as well as producing expensive hospitalization costs. In order to solve this problem, this paper summarizes the current situation and problems of risk prediction relevant to the readmission for patients. Combined with data mining technology, a risk assessment model for patients was designed, followed by the design and development of readmission risk assessment system for patients with cardiovascular disease. The risk assessment model includes three parts: risk prediction, clustering analysis and regression analysis of risk factors, which can automatically predicate the risk level and risk factors for the discharged patients in thirty days. The model was accurate 90.62% of the time. Combined the model assessment results with risk control knowledge base, a personalized health management and health guidance given by care workers can be put forward intelligently, which can not only help medical personnel in the rational allocation but also guide patients to carry out self-management better, resulting in the decrease of readmission rate.