A multi-agent ontologies-based clinical decision support system

TL;DR

This paper presents a multi-agent clinical decision support system that integrates heterogeneous medical knowledge bases using ontologies, enabling specialized agents to cooperate based on semantic relationships for improved clinical decision-making.

Contribution

It introduces a modular multi-agent framework utilizing ontologies for effective integration and cooperation among diverse clinical knowledge sources.

Findings

Enhanced integration of heterogeneous knowledge bases

Improved cooperation among specialized agents

Modular approach applicable to various clinical areas

Abstract

Clinical decision support systems combine knowledge and data from a variety of sources, represented by quantitative models based on stochastic methods, or qualitative based rather on expert heuristics and deductive reasoning. At the same time, case-based reasoning (CBR) memorizes and returns the experience of solving similar problems. The cooperation of heterogeneous clinical knowledge bases (knowledge objects, semantic distances, evaluation functions, logical rules, databases...) is based on medical ontologies. A multi-agent decision support system (MADSS) enables the integration and cooperation of agents specialized in different fields of knowledge (semiology, pharmacology, clinical cases, etc.). Each specialist agent operates a knowledge base defining the conduct to be maintained in conformity with the state of the art associated with an ontological basis that expresses the semantic relationships between the terms of the domain in question. Our approach is based on the specialization of agents adapted to the knowledge models used during the clinical steps and ontologies. This modular approach is suitable for the realization of MADSS in many areas.