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International Conference on Practical Applications of Agents and Multi-Agent Systems

PAAMS 2018: Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection pp 29–41Cite as

A Holonic Multi-agent Based Diagnostic Decision Support System for Computer-Aided History and Physical Examination

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10978))

Abstract

Available Medical Diagnosis systems (MDSs), including the state of the art, mainly focus on finding the perfect link between the patient’s medical history and their health knowledge. However, no matter how powerful they are in performing this action, it is always possible that the final strong deduction is based on some incomplete input. Prior to this process, a physician should literally perform the Differential Diagnosis (DDx), in which (S)he carefully listens to the symptoms explained by the patient, considers some potential diagnoses and then tries to gather enough evidence and supporting information to shrink the probability of the other candidates. In a patient encounter, this method is used in a process called the History and Physical examination (H&P). Only physicians and in some institutions, in order to compensate the shortage of the physicians, some specially trained nurses are qualified to perform this process. A system capable of guiding a focus H&P, however, will allow less experienced nurses to perform this process, and furthermore, can provide second opinions in critical cases. The DDx domain is in fact a holonic domain; hence, a MDS with holonic architecture could be able to perform this process. As the Holonic Medical Diagnosis System (HMDS), tends to cover the stages in the H&P, this system could also be added to available MDSs in order to provide them with the essential comprehensive input and allow their integration in the clinical workflow. This paper will demonstrate the performance of this system and concentrates on its learning process.

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Notes

  1. 1.

    The Euclidean distance between the DDPs of the DRAs.

  2. 2.

    In HMDS, the communications are solely done via the blackboard systems [30] of the DSAs.

  3. 3.

    The Euclidean distance between the corresponding DDP and the diagnosis request.

  4. 4.

    The holon identifier of the DRA acting for the final diagnosis, which is already updated considering the new observation.

  5. 5.

    If a super-holon, which has originally not participated in the diagnosis process, realizes that the disease given as the final diagnosis matches one of its members, it will assign the reward −1 (penalty) to this member.

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Author information

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

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

Editor information

Editors and Affiliations

  1. Centre National de la Recherche Scientifique, Grenoble, France

    Yves Demazeau

  2. Nanyang Technological University, Singapore, Singapore

    Bo An

  3. Universidad Politécnica de Madrid, Madrid, Spain

    Javier Bajo

  4. Universidad de Castilla La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

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Akbari, Z., Unland, R. (2018). A Holonic Multi-agent Based Diagnostic Decision Support System for Computer-Aided History and Physical Examination. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Lecture Notes in Computer Science(), vol 10978. Springer, Cham. https://doi.org/10.1007/978-3-319-94580-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-94580-4_3

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  • Print ISBN: 978-3-319-94579-8

  • Online ISBN: 978-3-319-94580-4

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