Distributed Medical Diagnosis with Abductive Logic Agents

We describe the application of a multi-agent system for the distributed diagnosis of infections within an hospital. Diagnosing infections within an hospital is a complex task that may require to collect data (e.g. analysis results, details on patient’s clinical history, diagnosis hypotheses) from several information sources (such as, for example, analysis laboratories, hospital wards. These sources act autonomously and they often have a partial knowledge about patients health, their clinical history, and medical information in general. As a natural consequence, this may lead a single entity (e.g., a specialist) to formulate incorrect diagnosis. In such a context, to obtain a correct diagnosis on the basis of information coming from different sources, a coordination mechanism is needed for the integration of collected data into a final diagnosis which should be compatible both with patient’s anamnesis and other knowledge (possibly distributed over the system) related to the clinical case. In the paper we face this problem by using abduction, which is a reasoning mechanism for formulating hypotheses in the case of incomplete knowledge, suitably etended to a multi-agent setting. In particular, we first apply ALIAS abductive agents to distributed diagnosis and show how the coordination mechanisms provided by such system are well suited when composing several (possibly partial) diagnosis into a final response, which is consistent with the knowledge of involved agents (i.e., hospital entities or specialist doctors). In the second part of the paper, we extend basic ALIAS coordination mechanisms towards probabilistic abduction. In this way, several (possibly partial) diagnosis obtained by probabilistic abductive reasoning can be merged into a final set of abductive diagnosis, each marked with a probability value.