An ontology-based fuzzy approach for encoding cognitive processes in medical decision making

Technological advances have broadened the capabilities and application of computers, so producing new smart knowledge-based systems aimed at emulating cognitive processes underlying medical decision making for improving patient care and health outcomes. In such a context, this paper proposes a novel approach to emulate cognitive processes in medical decision making, based on a fuzzy extension of standard semantic technologies, namely ontologies and rule engines. It consists into a formal model combining ontological primitives for reproducing information entities of the domain of interest and fuzzy linguistic statements for expressing uncertainty as relative grades of information. On the top of this model, cognitive processes underlying medical decision making are emulated by defining chains of hybrid logic rules involving both precise and vague information. An application case is provided to highlight how the approach can be used to encode medical decision making in managing patients affected by Chronic Obstructive Pulmonary Disease.