A study on diagnostic decision support system for metabolic syndrome care in cloud integrated clinic environments

Abstract

Facing the increasing demands and challenges in the area of chronic disease care, a number of studies on the healthcare system which can, whenever and wherever, extract and process patient data, have been conducted. Chronic diseases are the long-term diseases and require periodic monitoring, multidimensional quantitative analysis, and the classification of patients’ diagnostic information. Among the chronic diseases, metabolic syndrome (MS) that refers to a clustering of specific cardiovascular disease risk factors whose underlying pathology is thought to be related to insulin resistance is one of the major chronic diseases in many countries including Korea, because of its relationship with the incidence of cardiovascular disease (CVD) and type II diabetes mellitus (T2DM). Different from acute disease, chronic disease such MS requires long term care and its temporal information plays an important role to manage the status of disease. Health monitoring in out-of-hospital conditions, especially in the home environment, has drawn the attention of healthcare researchers and developers for a long time, because patients having chronic disease such as MS typically spend most time at home environment. In response to those increasing requirements, there have been a number of previous studies regarding MS, but most of them were focused on investigating the relationship between MS risk factors and the incidence of other chronic diseases such as CVD, coronary heart disease (CHD), and T2DM. Up to our knowledge, there has been no previous literature about quantifying the risk of MS, which is essential for predicting the incidence of MS in the future. To achieve this objective, it is imperative to overcome the well-known limitations of MS diagnostic definitions. There has been much effort to establish diagnostic criteria for MS, but it is known that current diagnostic criteria of MS have the following weaknesses such as no consideration for different importance among risk fa...