A holonic multi-agent system approach to sifferential diagnosis

Abstract

Medical diagnosis has always been a crucial and sophisticated matter, and despite its remarkable progresses, a reliable, cost-efficient, and fast computer-based medical diagnosis is still a challenge. There are two main types of computerized medical diagnosis systems: knowledge-based and non-knowledge-based systems. While the challenge of scalability and maintainability are the main shortcomings of the first group, the fact that the non-knowledge-based systems cannot explain the reasons for their conclusions makes them less appealing too. Moreover, even the most advanced systems fail to help the user in providing the right input. This work discusses the feasibility of the use of Holonic Multi-Agent Systems (HMASs) to tackle this problem, by performing differential diagnosis (DDx), that can improve diagnostic accuracy, and moreover guide the user in providing a more comprehensive input. The Holonic Medical Diagnosis System (HMDS), as a Multi-Agent System (MAS), offers the necessary reliability and scalability. By using Machine Learning (ML) techniques, it can also be self-adaptable to new findings. Furthermore, since it aims to perform DDx and tends to present the most likely diagnoses, the reasoning behind its output is also always implicitly recognizable. While the HMAS approach to DDx is the practical contribution of this work, the introduction of the ML techniques that support its functionality and dynamics is its theoretical contribution. Swarm Q-learning, as an off-policy reinforcement learning, is shown to be a perfect solution to this problem, and the Holonic-Q-learning technique is proposed, which can in general also be applied to any HMAS.