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A Strategic Approach Based on AND-OR Recommendation Trees for Updating Obsolete Information

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Modeling Decisions for Artificial Intelligence (MDAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13408))

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Abstract

Older adults usually require careful monitoring to detect healthcare problems at an early stage when the problems can be easily treated. Unfortunately, many members of this population are unable or unwilling to detect the existence of critical changes in their own health. One solution for caregivers is to start monitoring elderly patients, directly or via some data collection devices. Information describing a person’s health status is constantly evolving and may become obsolete and contradict other acquired information about the same person. So, it is of the utmost importance to monitor and update medical information scattered across healthcare institutions to support in-depth data analysis and achieve personalized healthcare. This study focuses on proposing a decision support system that gives recommendations on how to deal with obsolete personal information. The main objective of our system is to maintain up-to-date and consistent information about elderly patient in order to provide on-demand reliable information regarding the person’s current state. The approach outlined for this purpose is based on a polynomial-time algorithm build on top of a causal Bayesian network representing the elderly data. The result is given as a recommendation AND-OR tree with some accuracy level.

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Notes

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Acknowledgements

This work is supported and co-financed by the Ministry of Higher Education and Scientific Research of Tunisia. The experts who provided the estimates for the used causal Bayesian model and the University Hospital physicians who validated our scenarios are thanked for their participation.

Author information

Authors and Affiliations

  1. ISITCom, University of Sousse, 4011, Sousse, Tunisia

    Salma Chaieb

  2. ISAAS, University of Sfax, 1013, Sfax, Tunisia

    Ali Ben Mrad

  3. FSM, University of Monastir, 5000, Monastir, Tunisia

    Brahim Hnich

  4. CES Lab, ENIS, University of Sfax, 3038, Sfax, Tunisia

    Salma Chaieb, Ali Ben Mrad & Brahim Hnich

Authors
  1. Salma Chaieb
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  2. Ali Ben Mrad
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  3. Brahim Hnich
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Corresponding author

Correspondence to Salma Chaieb .

Editor information

Editors and Affiliations

  1. Umeå University, Umeå, Sweden

    Vicenç Torra

  2. Tamagawa University, Tokyo, Japan

    Yasuo Narukawa

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Chaieb, S., Ben Mrad, A., Hnich, B. (2022). A Strategic Approach Based on AND-OR Recommendation Trees for Updating Obsolete Information. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2022. Lecture Notes in Computer Science(), vol 13408. Springer, Cham. https://doi.org/10.1007/978-3-031-13448-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-13448-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13447-0

  • Online ISBN: 978-3-031-13448-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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