Modeling Medical Diagnosis Using a Comprehensive Cognitive Architecture

Medical diagnosis is accomplished by a set of complex cognitive processes requiring the iterative application of abduction, deduction, and induction. Previous research in computational modeling of medical diagnosis has had only limited success by defining sub-domains that offer a computationally tractable problem. However, the aspect of diagnostic reasoning requiring intelligence lies in the extraction of a well-structured problem from an ill-structured one. We propose an agent, based on the Learning Intelligent Distribution Agent ( LIDA) model of cognition, which utilizes deliberation, learning, and a neurologically inspired cognitive cycle. The proposed agent, MAX (for "Medical Agent X") will be equipped to comprehend clinical data in the context of its perceptual ontology and learned associations, and to construct, evaluate, and refine by investigation a differential diagnosis that progressively reduces the dimensionality of its search space with each iteration. Furthermore, the agent will appropriately modify its own ontology with experience and supervised instruction.