University of Oulu

M. Rostami, M. Oussalah, K. Berahmand and V. Farrahi, "Community Detection Algorithms in Healthcare Applications: A Systematic Review," in IEEE Access, vol. 11, pp. 30247-30272, 2023, doi: 10.1109/ACCESS.2023.3260652

Community detection algorithms in healthcare applications : a systematic review

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Author: Rostami, Mehrdad1; Oussalah, Mourad1,2; Berahmand, Kamal3;
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
3School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, QLD, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 5.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-09-22


Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 11
Pages: 30247 - 30272
DOI: 10.1109/ACCESS.2023.3260652
Type of Publication: A2 Review article in a scientific journal
Field of Science: 113 Computer and information sciences
217 Medical engineering
Funding: This work was supported in part by the Academy of Finland Profi5 DigiHealth-Project, a Strategic Profiling Program at the University of Oulu under Project 326291; and in part by the Ministry of Education and Culture under Grant OKM/20/626/2022 and Grant OKM/76/626/2022.
Copyright information: © The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see