Main Article Content

Abstract

Healthcare issues are currently the most researched issues worldwide. Many healthcare researchers collaborate with non-healthcare researchers to improve the quality of healthcare. The knowledge graph is a widely used computer science and mathematics approach to solve healthcare issues. It can model the relationship between events to build new knowledge. Hence, a comprehensive study on knowledge graph modeling in healthcare was conducted in this study. The research methodologies in this study were: (1) article retrieval and general bibliometric analysis; (2) visualization of research distribution; and (3) research recommendations. In the last three years, 867 articles were retrieved from three databases. The citation metrics analysis was also conducted to determine the quality level of articles retrieval. An analysis was conducted using network and density visualization related to the relationship between research topics and trends. The final results in this paper are recommendations for research topics and research titles related to knowledge graph modeling in healthcare.

Keywords

Analisis bibliometrik Graf pengetahuan Kesehatan Bibliometric analysis Knowledge graph Healthcare

Article Details

Author Biographies

Muhammad Furqon, Universitas Jember

Informatics Department

Nina Najwa, Politeknik Caltex Riau

Information System Department

Deny Hermansyah, Universitas Hayam Wuruk Perbanas

Informatics Department

Mohammad Zarkasi, Universitas Jember

Information Technology Department
How to Cite
Furqon, M., Najwa, N., Hermansyah, D., & Zarkasi, M. (2022). Knowledge Graph Modeling in Healthcare: A Bibliometric Analysis. Jurnal Komputer Terapan, 8(1), 113–122. https://doi.org/10.35143/jkt.v8i1.5373

References

  1. L. C. Shimomura, R. S. Oyamada, M. R. Vieira, and D. S. Kaster, “A survey on graph-based methods for similarity searches in metric spaces,†Information Systems, vol. 95, no. xxxx, p. 101507, 2021, doi: 10.1016/j.is.2020.101507.
  2. M. E. Bales and S. B. Johnson, “Graph theoretic modeling of large-scale semantic networks,†Journal of Biomedical Informatics, vol. 39, no. 4, pp. 451–464, 2006, doi: 10.1016/j.jbi.2005.10.007.
  3. R. Alguliyev, R. Aliguliyev, and F. Yusifov, “Graph modelling for tracking the COVID-19 pandemic spread,†Infect Dis Model, vol. 6, pp. 112–122, 2021, doi: 10.1016/j.idm.2020.12.002.
  4. M. A. Furqon, N. A. Rakhmawati, and F. Mahananto, “Heart failure decision support system using semantic web approach,†Proceedings - 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018, no. July 2019, pp. 298–303, 2018, doi: 10.1109/ICITISEE.2018.8720984.
  5. B. Shen, T. Guan, J. Ma, L. Yang, and Y. Liu, “Social Network Research Hotspots and Trends in Public Health: A Bibliometric and Visual Analysis,†Public Health in Practice, vol. 2, no. June, p. 100155, 2021, doi: 10.1016/j.puhip.2021.100155.
  6. T. M. Abuhay, Y. G. Nigatie, and S. v. Kovalchuk, “Towards Predicting Trend of Scientific Research Topics using Topic Modeling,†Procedia Computer Science, vol. 136, pp. 304–310, 2018, doi: 10.1016/j.procs.2018.08.284.
  7. T. Saheb, B. Amini, and F. Kiaei Alamdari, “Quantitative analysis of the development of digital marketing field: Bibliometric analysis and network mapping,†International Journal of Information Management Data Insights, vol. 1, no. 2, p. 100018, 2021, doi: 10.1016/j.jjimei.2021.100018.
  8. M. A. Furqon, G. Faisal, and N. F. Najwa, “Graph Database Modelling on Malay Architecture IFC Data,†2021.
  9. J. A. Moral-Muñoz, E. Herrera-Viedma, A. Santisteban-Espejo, and M. J. Cobo, “Software tools for conducting bibliometric analysis in science: An up-to-date review,†Profesional de la Informacion, vol. 29, no. 1, pp. 1–20, 2020, doi: 10.3145/epi.2020.ene.03.
  10. I. Hamidah, Sriyono, and M. Hudha, “A Bibliometric analysis of Covid-19 research using VOSviewer,†Indonesian Journal of Science & Technology, vol. 5, no. 2, pp. 209–216, 2020.
  11. X. Chen, S. Jia, and Y. Xiang, “A review: Knowledge reasoning over knowledge graph,†Expert Systems with Applications, vol. 141, 2020, doi: 10.1016/j.eswa.2019.112948.
  12. O. Klapka and A. Slaby, “Visual Analysis of Search Results in Scopus Database,†in Digital Libraries for Open Knowledge, 2018, pp. 340–343.
  13. E. Gibson, “NiftyNet: a deep-learning platform for medical imaging,†Computer Methods and Programs in Biomedicine, vol. 158, pp. 113–122, 2018, doi: 10.1016/j.cmpb.2018.01.025.
  14. X. Chen, L. Huang, D. Xie, and Q. Zhao, “EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction.,†Cell death & disease, vol. 9, no. 1, p. 3, 2018, doi: 10.1038/s41419-017-0003-x.
  15. S. Parisot, “Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease,†Medical Image Analysis, vol. 48, pp. 117–130, 2018, doi: 10.1016/j.media.2018.06.001.
  16. L. Warrington, “Electronic systems for patients to report and manage side effects of cancer treatment: Systematic review,†Journal of Medical Internet Research, vol. 21, no. 1, 2019, doi: 10.2196/10875.
  17. I. Tobore, “Deep learning intervention for health care challenges: Some biomedical domain considerations,†JMIR Mhealth Uhealth, vol. 7, no. 8, 2019, doi: 10.2196/11966.
  18. X. Zheng, “Accelerating health data sharing: A solution based on the internet of things and distributed ledger technologies,†Journal of Medical Internet Research, vol. 21, no. 6, 2019, doi: 10.2196/13583.
  19. Y. Zhao, “Retinal artery and vein classification via dominant sets clustering-based vascular topology estimation,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11071, pp. 56–64, 2018, doi: 10.1007/978-3-030-00934-2_7.
  20. A. Sharma, “Impact of isolation precautions on quality of life: a meta-analysis,†Journal of Hospital Infection, vol. 105, no. 1, pp. 35–42, 2020, doi: 10.1016/j.jhin.2020.02.004.
  21. J. C. Carvaillo, “Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach,†Environ Health Perspect, vol. 127, no. 4, p. 47005, 2019, doi: 10.1289/EHP4200.
  22. A. A. Guerra, “Building the national database of health centred on the individual: Administrative and epidemiological record linkage - Brazil, 2000-2015,†International Journal of Population Data Science, vol. 3, no. 1, 2018, doi: 10.23889/ijpds.v3i1.446.
  23. G. Bakal, P. Talari, E. v Kakani, and R. Kavuluru, “Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.,†J Biomed Inform, vol. 82, pp. 189–199, 2018, doi: 10.1016/j.jbi.2018.05.003.
  24. J. C. Brunson, “Applications of network analysis to routinely collected health care data: A systematic review,†Journal of the American Medical Informatics Association, vol. 25, no. 2, pp. 210–221, 2018, doi: 10.1093/jamia/ocx052.
  25. T. M. Deist et al., “Distributed learning on 20 000+ lung cancer patients - The Personal Health Train.,†Radiother Oncol, vol. 144, pp. 189–200, 2020, doi: 10.1016/j.radonc.2019.11.019.
  26. J. A. Shaffer, I. M. Kronish, L. Falzon, Y. K. Cheung, and K. W. Davidson, “N-of-1 Randomized Intervention Trials in Health Psychology: A Systematic Review and Methodology Critique.,†Ann Behav Med, vol. 52, no. 9, pp. 731–742, 2018, doi: 10.1093/abm/kax026.
  27. M. Rostami, S. Forouzandeh, K. Berahmand, and M. Soltani, “Integration of multi-objective PSO based feature selection and node centrality for medical datasets.,†Genomics, vol. 112, no. 6, pp. 4370–4384, 2020, doi: 10.1016/j.ygeno.2020.07.027.
  28. W. Zhang, “Efficacy and safety of photodynamic therapy for cervical intraepithelial neoplasia and human papilloma virus infection: A systematic review and meta-analysis of randomized clinical trials,†Medicine (United States), vol. 97, no. 21, 2018, doi: 10.1097/MD.0000000000010864.
  29. M. A. Mahdi, “A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers,†Lecture Notes in Networks and Systems, vol. 81, pp. 320–339, 2020, doi: 10.1007/978-3-030-23672-4_23.
  30. M. Daoud, “Automatic superpixel-based segmentation method for breast ultrasound images,†Expert Systems with Applications, vol. 121, pp. 78–96, 2019, doi: 10.1016/j.eswa.2018.11.024.
  31. L. Li, “Real-world data medical knowledge graph: construction and applications,†Artificial Intelligence in Medicine, vol. 103, 2020, doi: 10.1016/j.artmed.2020.101817.
  32. K. Thornton, “Using shape expressions (ShEx) to share rdf data models and to guide curation with rigorous validation,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11503, pp. 606–620, 2019, doi: 10.1007/978-3-030-21348-0_39.
  33. Y. Khan, “One size does not fit all: Querying web polystores,†IEEE Access, vol. 7, pp. 9598–9617, 2019, doi: 10.1109/ACCESS.2018.2888601.
  34. D. Fozoonmayeh, “A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning,†Journal of Medical Systems, vol. 44, no. 4, 2020, doi: 10.1007/s10916-019-1518-8.
  35. P. Lara-Navarra, H. Falciani, E. A. Sánchez-Pérez, and A. Ferrer-Sapena, “Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection.,†Int J Environ Res Public Health, vol. 17, no. 3, 2020, doi: 10.3390/ijerph17031066.
  36. E. B. Panganiban, “Real-Time Intelligent Healthcare Monitoring and Diagnosis System Through Deep Learning and Segmented Analysis,†IFMBE Proceedings, vol. 74, pp. 15–25, 2020, doi: 10.1007/978-3-030-30636-6_3.

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