Identifying design issues related to the knowledge bases of medical decision support systems

The modern medical diagnostic systems are based on the techniques using digital data formats – a natural feed for the computer based systems. With the use of modern diagnostic techniques the diagnosis process is becoming more complex as many diseases seem to have the same pre-symptoms at early stages. And of course computer based systems require more efficient and effective ways to identify such complexities. However, the existing formalisms for knowledge representation, tools and technologies, learning and reasoning strategies seem inadequate to create meaningful relationship among the entities of medical data i.e. diseases, symptoms and medicine etc. This inadequacy actually is due to the poor design of the knowledge base of the medical system and leads the medical systems towards inaccurate diagnosis. This thesis discusses the limitations and issues specific to the design factors of the knowledge base and suggests that instead of using the deficient approaches and tools for representing, learning and retrieving the accurate knowledge, use of semantic web tools and techniques should be adopted. Design by contract approach may be suitable for establishing the relationships between the diseases and symptoms. The relationship between diseases and symptoms and their invariants can be represented more meaningfully using semantic web. This can lead to more concrete diagnosis, by overcoming the deficiencies and limitations of traditional approaches and tools