A method for identifying temporal progress of chronic disease using chronological clustering

The development of an integrated and personalized healthcare system is becoming an important issue in the modern healthcare industry. One of main objectives of integrated healthcare system is to effectively manage patients having chronic disease. Different from acute disease, chronic disease requires long term care and its temporal information plays an important role to manage the status of disease. Thus, a patient having chronic disease needs to visit the hospital periodically, which generates large volume of medical data. Among the various chronic diseases, metabolic syndrome has become a major public healthcare issue in many countries. There have been efforts to develop a metabolic syndrome risk quantification and prediction model and to integrate them into personalized healthcare system, so as to predict the risk of having metabolic syndrome in the future. However, the development of methods for temporal progress management of metabolic syndrome has not been widely investigated. In this paper, we propose a method for identifying a temporal progress and patient's status of metabolic syndrome. Further, the effectiveness of the proposed method is evaluated using a sample patient data while emphasizing the capability to identify chronological changes of metabolic syndrome status.