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A Holonic Multi-Agent System Approach to Differential Diagnosis

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Book cover Multiagent System Technologies (MATES 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10413))

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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.

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Notes

  1. 1.

    Definition proposed by Robert Hayward of the Centre for Health Evidence.

  2. 2.

    The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [56] is one of the best algorithms for this issue. In [57] a simple and effective method for automatically detecting the input parameter of DBSCAN is presented, which helps best to deal with complicated data such as diseases.

  3. 3.

    A mapping \( T:X \to X \) is a contraction on a metric space \( (X,d) \), if there exists a constant \( c \), with \( 0 \le c < 1 \), such that \( d\left( {T\left( x \right),T\left( y \right)} \right) \le c. d(x,y) \) for all \( x,y \in X \) [58].

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Acknowledgement

The authors gratefully acknowledge the informative and encouraging discussions with Dr. Farzad Fakouri, MD on the medical aspects of the project, and would like to express appreciation and gratitude for his knowledgeable insight and expertise, that greatly assisted the research.

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Authors and Affiliations

  1. Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Essen, Germany

    Zohreh Akbari & Rainer Unland

  2. Department of Information Systems, Poznan University of Economics, Poznan, Poland

    Rainer Unland

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  1. Zohreh Akbari
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  2. Rainer Unland
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Correspondence to Zohreh Akbari .

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Editors and Affiliations

  1. University of Trier, Trier, Germany

    Jan Ole Berndt

  2. University of Vienna, Vienna, Austria

    Paolo Petta

  3. Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Essen, Nordrhein-Westfalen, Germany

    Rainer Unland

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Akbari, Z., Unland, R. (2017). A Holonic Multi-Agent System Approach to Differential Diagnosis. In: Berndt, J., Petta, P., Unland, R. (eds) Multiagent System Technologies. MATES 2017. Lecture Notes in Computer Science(), vol 10413. Springer, Cham. https://doi.org/10.1007/978-3-319-64798-2_17

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