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Next Generation Hybrid Intelligent Medical Diagnosis Systems

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10636))

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Abstract

Many medical diagnosis problems (MDPs) are difficult to be solved by physicians. Frequently, the difficult MDPs solving by physicians require the assistance of the medical computing systems, which many times should be intelligent. Many papers presented in the specialized literature prove, that the intelligence of a system (frequently agent-based) can offer advantages in the MDPs solving versus a system that does not have such intelligence. Cooperative hybrid (human-machine) medical diagnosis systems seem to be well suited for the solving of many difficult MDPs. A difficult aspect in the design of such systems consists in the establishment of how to combine in an optimal way the humans and intelligent systems interoperation in order to solve the undertaken problems in the most efficient way. With this purpose, a novel hybrid medical system, called Intelligent Medical Hybrid System (IntHybMediSys) is proposed in this paper, a system which combines efficiently the humans and computing systems advantages in the problem-solving. We give a definition to the Difficult Medical Diagnosis Problem Solving Intelligence. IntHybMediSys is a highly complex hybrid system composed of physicians and intelligent agents that can interoperate intelligently in different points of decision in order to solve efficiently very difficult medical diagnosis problems. IntHybMediSys is able to handle emergent information that rise during the medical problems solving that allows the precise establishment of the most efficient contributor (a physician or an artificial agent) at each contribution during a problem-solving. This kind of problem-solving has as an effect the increase of accuracy of the elaborated diagnostic.

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Acknowledgment

Laszlo Barna Iantovics acknowledge the support of the COROFLOW project PN-III-P2-2.1-BG-2016-0343, Contract: 114BG/2016.

Author information

Authors and Affiliations

  1. Istanbul University, Istanbul, Turkey

    Sabri Arik

  2. Petru Maior University, Tirgu Mures, Romania

    Laszlo Barna Iantovics

Authors
  1. Sabri Arik
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  2. Laszlo Barna Iantovics
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Corresponding author

Correspondence to Laszlo Barna Iantovics .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Arik, S., Iantovics, L.B. (2017). Next Generation Hybrid Intelligent Medical Diagnosis Systems. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_92

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_92

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70089-2

  • Online ISBN: 978-3-319-70090-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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