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Fuzzy HealthIoT Ontology for Comorbidity Treatment

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Model and Data Engineering (MEDI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14396))

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Abstract

The utilization of Internet of Things (IoT) technologies in the medical field has resulted in the development of numerous intelligent applications and devices for health monitoring. These devices generate a large amount of data, which is collected in various formats and often exhibits uncertainty. As a consequence, interpreting and sharing these data among various medical systems poses a significant challenge. To address this challenge, ontologies, particularly fuzzy ontologies, have been employed to ensure semantic interoperability among these systems and enable them to comprehend, share, and effectively utilize fuzzy data. Therefore, to address these issues, the main objective of this paper is the fuzzification of the HealthIoT ontology. Fuzzification includes concepts related to the medical field and the IoT domain (connected objects). We showcased the application of the Fuzzy-HealthIoT ontology in a specific use case in healthcare, specifically focusing on patient comorbidity management.

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Notes

  1. 1.

    https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction.

  2. 2.

    https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset.

  3. 3.

    https://saref.etsi.org/saref4ehaw/v1.1.1/.

  4. 4.

    https://protegewiki.stanford.edu/wiki/FuzzyOWL2.

  5. 5.

    https://openrefine.org/.

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Acknowledgements

This work is partially funded by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with the Universidad Politécnica de Madrid in the Excellence Programme for University Teaching Staf, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

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Authors and Affiliations

  1. Ontology Engineering Group (OEG), Universidad Politécnica de Madrid, Madrid, Spain

    Ahlem Rhayem & Raúl García-Castro

  2. LRDSI Laboratory, Faculty of Sciences, University of Blida 1, Ouled Yaïch, Algeria

    Ishak Riali & Messaouda Fareh

  3. Miracl Laboratory, University of Sfax, Sfax, Tunisia

    Mohamed Ben Ahmed Mhiri & Faiez Gargouri

Authors
  1. Ahlem Rhayem
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  2. Ishak Riali
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  3. Mohamed Ben Ahmed Mhiri
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  4. Messaouda Fareh
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  5. Raúl García-Castro
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  6. Faiez Gargouri
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Corresponding authors

Correspondence to Ahlem Rhayem or Ishak Riali .

Editor information

Editors and Affiliations

  1. Bordeaux INP, Talence, France

    Mohamed Mosbah

  2. Dublin City University, Dublin, Ireland

    Tahar Kechadi

  3. ENSMA, Poitiers, France

    Ladjel Bellatreche

  4. University of Sfax, Sfax, Tunisia

    Faiez Gargouri

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Rhayem, A., Riali, I., Mhiri, M.B.A., Fareh, M., García-Castro, R., Gargouri, F. (2024). Fuzzy HealthIoT Ontology for Comorbidity Treatment. In: Mosbah, M., Kechadi, T., Bellatreche, L., Gargouri, F. (eds) Model and Data Engineering. MEDI 2023. Lecture Notes in Computer Science, vol 14396. Springer, Cham. https://doi.org/10.1007/978-3-031-49333-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-49333-1_17

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  • Online ISBN: 978-3-031-49333-1

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