Agent based decision support systems in medicine

Abstract

Embedding Machine Learning technology into Agent Driven Diagnosis Systems adds a new potential to the realm of Medicine, and in particular to the imagiology one. However, despite all the research done in the last years on the development of new methodologies for problem solving, in terms of the design of MultiAgent Systems (MAS) there is none where both the agent and the organizational view can be modelled. Current multi-agent approaches to problem solving either take a centralist, static approach to organizational design or take an emergent view in which agent interactions are not pre-determined, thus making it impossible to make any predictions on the behavior of the whole systems. Most of them also lack a model of the norms in the environment that should rule the behaviour of the agent society as a whole and/or the actions of the individuals. In this paper, it is proposed not only a framework for modelling and run agent organizations, but also to depict the different components of such societies. To illustrate these premises, we will evoke a society with one modality, the Axial Computed Tomography one, where two different but complementary computational paradigms, the Artificial Neural Networks and the Case Based Reasoning are object of attention.