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. 2021 Mar 17;18(6):3099.
doi: 10.3390/ijerph18063099.

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

Maikel Luis Kolling  1 , Leonardo B Furstenau  2 , Michele Kremer Sott  1 , Bruna Rabaioli  3 , Pedro Henrique Ulmi  4 , Nicola Luigi Bragazzi  5 , Leonel Pablo Carvalho Tedesco  1   4
Affiliations

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

Maikel Luis Kolling et al. Int J Environ Res Public Health. .

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: SciMAT; bibliometrics; co-word analysis; data mining; healthcare 4.0; industry 4.0; science mapping; strategic intelligence.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Strategic diagram (a). Thematic network structure (b). Thematic evolution structure (c).
Figure 2
Figure 2
Workflow of the bibliometric performance and network analysis (BPNA).
Figure 3
Figure 3
Number of publications over time (1995–July 2020).
Figure 4
Figure 4
Strategic diagram of data mining in healthcare (1995–July 2020).
Figure 5
Figure 5
Thematic network structure of mining in healthcare (1995–July 2020). (a) The cluster ‘NEURAL-NETWORKS’. (b) The cluster ‘CANCER’. (c) The cluster ‘ELECTRONIC-HEALTH-RECORDS’. (d) The cluster ‘DIABETES-MELLITUS’. (e) The cluster ‘BREAST-CANCER’. (f) The cluster ‘ALZHEIMER’S DISEASE’. (g) The cluster ‘DEPRESSION’. (h) The cluster ‘RANDOM-FOREST’.
Figure 6
Figure 6
Thematic evolution structure of mining in healthcare (1995–July 2020).
See this image and copyright information in PMC

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