An online belief rule-based group clinical decision support system

Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.EThOS - Electronic Theses Online ServiceManchester Business SchoolGBUnited Kingdo

International Conference on Computer Systems and Technologies- CompSysTech’07 Knowledge Representation in Systems with Natural Language Interface

Abstract: This paper presents a short analysis of the basic methods for knowledge representation in the systems with artificial intelligence. The pros and cons of every method have been reviewed. Based on the analysis made in the paper, a frame knowledge representation for the application has been chosen. The application itself is an analyzer of natural language sentences. An example illustrates the capabilities of the created analyzer, which is both problem- and language- independent