Medical Diagnosis Decision Support HMAS under Uncertainty HMDSuU

Fast, reliable, and correct medical diagnostics is of utter importance in today’s world where diseases can spread quickly. For this reason, we have developed a medical diagnosis system that is based on multi agent system theory, the holonic paradigm, and swarm intelligence techniques. More specifically, a huge number of comparatively simple agents form the basis of our system. In order to provide a solid medical diagnosis always a set of relevant agents needs to work together. These agents will provide a huge set of possible solutions, which need to be evaluated in order to conclude. The paradigm of swarm intelligence implies that a set of comparatively simple entities produces sophisticated and highly reliable results. In our scenario, it means that our agents are not provided with a real world model; i.e., it has only a very limited understanding on health issues and the process of medical diagnosis. This puts a huge burden on the decision process.

This paper concentrate on the decision process within our system and will present our ideas, which are based on decision theory, and here, especially, on Bayesian probability since, among others, uncertainty is inherent feature of a medical diagnosis process. The presented approach focuses on reaching the optimal medical diagnosis with the minimum risk under the given uncertainty. Additional factors that play an important role are the required time for the decision process and the produced costs.