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Adapted Visual Analytics Process for Intelligent Decision-Making: Application in a Medical Context

Author

Listed:
  • Hela Ltifi

    (Research Groups in Intelligent Machines, University of Sfax National School of Engineers (ENIS), BP 1173, Sfax, 3038, Tunisia)

  • Emna Benmohamed

    (Research Groups in Intelligent Machines, University of Sfax National School of Engineers (ENIS), BP 1173, Sfax, 3038, Tunisia)

  • Christophe Kolski

    (#x2020;LAMIH-UMR CNRS 8201, Université Polytechnique Hauts-de-France, Valenciennes, France)

  • Mounir Ben Ayed

    (Research Groups in Intelligent Machines, University of Sfax National School of Engineers (ENIS), BP 1173, Sfax, 3038, Tunisia)

Abstract

The theoretical and practical researches on Visual Analytics for intelligent decision-making tasks have remarkably advanced in the past few years. Intelligent Decision Support Systems (IDSS) introduce effective and efficient paths from raw data to decision by involving visualization and data mining technologies. Data mining-based DSS produces potentially interesting patterns from data. The transition from extracted patterns to knowledge is a delicate task. In this context, we propose to adapt a common visual analytics process for creating a path that enables the user (decision-maker) to automatically explore and visually extract insights by interacting with the patterns. This proposal is inspired from integrating traditional visual analytics concepts with the mental model of knowledge visualization. The idea is to combine an automatic and visual analysis of patterns to generate knowledge for the purpose of decision-making. To validate our proposal, we have applied it to a medical case study for the fight against Nosocomial Infections in Intensive Care Units. The developed platform was evaluated according to the utility and usability dimensions.

Suggested Citation

  • Hela Ltifi & Emna Benmohamed & Christophe Kolski & Mounir Ben Ayed, 2020. "Adapted Visual Analytics Process for Intelligent Decision-Making: Application in a Medical Context," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 241-282, February.
    • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:01:n:s0219622019500470
    DOI: 10.1142/S0219622019500470
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    References listed on IDEAS

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    1. Jasser Al-Kassab & Zied M. Ouertani & Giovanni Schiuma & Andy Neely, 2014. "Information visualization to support management decisions," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 407-428.
    2. S. Bajracharya & G. Carenini & B. Chamberlain & K. Chen & D. Klein & D. Poole & H. Taheri & G. Öberg, 2018. "Interactive Visualization for Group Decision Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1839-1864, November.
    3. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    4. Michael Hahsler & Anurag Nagar, 2018. "Discovering Patterns in Gene Ontology Using Association Rule Mining," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(3), pages 99-101, April.
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    Cited by:

    1. Moussa Larbani & Po Lung Yu, 2020. "Empowering Data Mining Sciences by Habitual Domains Theory, Part I: The Concept of Wonderful Solution," Annals of Data Science, Springer, vol. 7(3), pages 373-397, September.

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