Designing and implementing a collaborative health knowledge system. (15th July 2019) - Giannoulis, Michail; Kondylakis, Haridimos; Marakakis, Emmanouil
Highlights: A novel intelligent, collaborative health knowledge system supporting diagnosis. Temporal rule and knowledge management. An inference engine handling the embedded time and uncertainty. A conflict detection policy for supporting rule generation. A well-designed and user-friendly web interface with strong collaboration features. Abstract: The huge diversity and quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning This paper elaborates on the design and development of COSMOS, an intelligent system which supports a) collaboration of experts for developing common knowledge bases and b) diagnosis derivation which takes into account time and uncertainty. The health domain is used for illustration and discussion of the features of our approach. Initially, we present the syntax and the semantics of the rules which incorporate time (temporal rules) and the data items upon which reasoning is performed. Then we introduce its inference engine, able to perform reasoning on top of rules and data, handling also the embedded time and uncertainty. We proceed further to define a conflict detection policy for supporting the difficult and error prone task of rule generation. The complexities of the aforementioned tasks are hidden from users via a well-designed and user friendly web interface that possesses strong collaboration features enabling multiple experts to work on defining a rule and on developing a common knowledge base. Evaluation of COSMOS has been performed using a) students studying expert systems and b) health experts in order to demonstrate the usability of the approach and the considerable advantages gained. To the best of our knowledge, COSMOS is one of the very few systems combining temporal rules, a powerful inference engine handling uncertainty and conflict detection and progresses beyond state of the art by adopting strong collaboration features and paradigms.