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A Real-World Clinical Data Mining of Post COVID-19 Patients

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 182))

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Abstract

Analysis of real-world clinical data, which includes multiple heterogenous categories of attributes, requires a well-designed data-mining process to obtain meaningful information. We explored the application of several data-mining methods on a real-world dataset of patients diagnosed with COVID-19, with emphasis on cohort selection with maximum data availability, and feature selection based on their correlation with clinical data. Two data mining platforms (Orange data-mining platform coupled with LRNet method, and Waikato environment for knowledge analysis) were used for finding important attributes associated with post COVID-19 symptoms from the multiple modalities of this cohort and evaluating the ability of these attributes to separate patients into clusters. We introduced a dynamic method of inclusion and exclusion, as well as outlier selection, which maximized the knowledge extracted from this real-world dataset. We also demonstrated that a comprehensive first-view of this dataset was only possible by applying multiple methods for dimensionality reduction and feature selection.

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Notes

  1. 1.

    Can be accessed at: https://homel.vsb.cz/~kud007/lrnet_files (last accessed 23 June 2023).

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Acknowledgments

The study was performed in accordance with the ethical standards of the institutional or national research committee and respected the 1964 Helsinki Declaration and its later amendments or comparable relevant ethical standards and was approved by the Institutional Ethics Committee of Palacký University Olomouc and University Hospital Olomouc.

This research was funded by SGS, VSB-Technical University of Ostrava (grant number SP2023/076) and the Ministry of Health of the Czech Republic (grant number NU22-A-105), and in part by IGA-LFUP-2023-010 and FNOL-00098892.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Arootin Gharibian & Milos Kudelka

  2. Department of Pulmonary Diseases and Tuberculosis, Faculty of Medicine and Dentistry, Palacký University and University Hospital, Olomouc, Czech Republic

    Jan Mizera, Samuel Genzor & Milan Sova

  3. Department of Immunology, Faculty of Medicine and Dentistry, Palacký University and University Hospital, Olomouc, Czech Republic

    Bishu Shrestha, Zuzana Mikulkova & Eva Kriegova

Authors
  1. Arootin Gharibian
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  2. Jan Mizera
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  4. Zuzana Mikulkova
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  5. Samuel Genzor
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  6. Milan Sova
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  7. Milos Kudelka
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  8. Eva Kriegova
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Correspondence to Eva Kriegova .

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Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Gharibian, A. et al. (2023). A Real-World Clinical Data Mining of Post COVID-19 Patients. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_41

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