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Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

Author

Listed:
  • Maikel Luis Kolling

    (Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil)

  • Leonardo B. Furstenau

    (Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil)

  • Michele Kremer Sott

    (Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil)

  • Bruna Rabaioli

    (Department of Medicine, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil)

  • Pedro Henrique Ulmi

    (Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil)

  • Nicola Luigi Bragazzi

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Leonel Pablo Carvalho Tedesco

    (Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil
    Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil)

Abstract

In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes (‘NEURAL-NETWORKS’, ‘CANCER’, ‘ELETRONIC-HEALTH-RECORDS’, ‘DIABETES-MELLITUS’, ‘ALZHEIMER’S-DISEASE’, ‘BREAST-CANCER’, ‘DEPRESSION’, and ‘RANDOM-FOREST’) are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field’s evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.

Suggested Citation

  • Maikel Luis Kolling & Leonardo B. Furstenau & Michele Kremer Sott & Bruna Rabaioli & Pedro Henrique Ulmi & Nicola Luigi Bragazzi & Leonel Pablo Carvalho Tedesco, 2021. "Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development," IJERPH, MDPI, vol. 18(6), pages 1-20, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3099-:d:519054
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    References listed on IDEAS

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    2. Ricardo Abejón, 2022. "A Bibliometric Analysis of Research on Selenium in Drinking Water during the 1990–2021 Period: Treatment Options for Selenium Removal," IJERPH, MDPI, vol. 19(10), pages 1-38, May.

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