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

Maikel Luis Kolling, Leonardo B. Furstenau, Michele Kremer Sott, Bruna Rabaioli, Pedro Henrique Ulmi, Nicola Luigi Bragazzi and Leonel Pablo Carvalho Tedesco
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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

IJERPH, 2021, vol. 18, issue 6, 1-20

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.

Keywords: data mining; industry 4.0; healthcare 4.0; bibliometrics; science mapping; strategic intelligence; co-word analysis; SciMAT (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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