Using concept based OWL2 rules in risk assessment of diabetic patients

A complete patient health record is a mandatory requirement to conduct an efficient diagnosis. Even though the traditional method of collecting health records is through peer to peer interview between doctor and patient, for a very long time, computers were used by medical professionals in diagnosing the diseases and in clinical decision making processes. Such clinical decision support systems (CDSS) helped the doctors to have more accurate and precise diagnosis. In the past decades, development of CDSS based on semantic web technologies is high. Ontology is one of the most powerful tools to encode medical knowledge semantically. We have developed an ontology based CDSS to assess the risk factors and to provide appropriate treatment suggestions for diabetic patients. The design of knowledge base and the reasoning mechanism is very critical in ontology based CDSS. If the knowledge base is incomplete or/and the reasoning mechanisms are ambiguous, then it will result in incomplete and imprecise diagnosis. In this paper, we present an overview of our CDSS and focus on the risk assessment related to cardiovascular, sexual, alcohol, smoking, and physical activity factors of diabetic patients, which are the core risk elements in diabetics. OWL2 rule language is used to implement the clinical guidelines which are used to calculate the risk of diabetic patients in the above factors.