Knowledge capturing in autonomous system design for chronic disease risk assessment

Recent years has witnessed a rapid increase in chronic diseases that populations are suffering from. According to the World Health Organization (WHO), these diseases, such as heart disease, stroke, cancer, chronic respiratory diseases and diabetes, are without doubt the leading cause of mortality in the world, representing 60% of all deaths [1]. Diabetes, however, is one of these chronic diseases that started to increase for the past decades and effecting the life of a large number of people around the world, According to World Health Organization latest record in 2011, there are one in ten adults have diabetes. It was also found that diabetes causes approximately 10% of all deaths globally each year. With respect to the current life style, this percentage is most likely to increase exponentially within the coming few years. However, in order to reduce the effect of such a disease, patients need to be monitored closely and measures should be put into place to control diabetes level. This awareness requires the deployment of a supporting system that assists both Healthcare providers as well as patients. This, in turn, is expecting to increase the load on healthcare providers. The work in this paper presents an approach of building an autonomous system that can be integrated as part of a wider eHealth application. Knowledge capturing plays a vital role for constructing such a system. Hence, this research concentrates on discovering knowledge that held by physicians, and then externalizes tacit knowledge that can be codified in order to form the bases of the autonomous system.