Copyright
©The Author(s) 2025.
World J Transplant. Mar 18, 2025; 15(1): 99642
Published online Mar 18, 2025. doi: 10.5500/wjt.v15.i1.99642
Published online Mar 18, 2025. doi: 10.5500/wjt.v15.i1.99642
Figure 1
Annual publications and citations of machine learning articles in the field of solid organ transplantation (2007-2023).
Figure 2 The network visualization of the top contributing countries and their interconnections.
Each circle represents a country, and the size of the circles indicates the frequency of occurrence. Larger circles indicate that the country publishes more frequently. Countries included in the same cluster, which occurred frequently together, are displayed in the same color. The distance and line thickness between two circles represent the degree of the relationship between countries.
Figure 3 The network visualization of the top contributed authors in the field of machine learning and solid organ transplantation and their interconnections.
Each square represents an author, and the size of the square represents the frequency of occurrence. Larger squares indicate that the author appears more frequently. Authors included in the same cluster, who occurred frequently together, are displayed in the same color. The distance and line thickness between the two squares show the degree of the relationship.
Figure 4 Cluster visualization of the most frequently occurring keywords and their interconnections.
Each circle represents a keyword with a label, and the size of the circles indicates the frequency of occurrence. Larger circles indicate that the keyword appears more frequently. Keywords included in the same cluster, which occur frequently together, are displayed in the same color. The distance and line thickness between the two circles show the degree of the relationship.
- Citation: Rawashdeh B, Al-abdallat H, Arpali E, Thomas B, Dunn TB, Cooper M. Machine learning in solid organ transplantation: Charting the evolving landscape. World J Transplant 2025; 15(1): 99642
- URL: https://www.wjgnet.com/2220-3230/full/v15/i1/99642.htm
- DOI: https://dx.doi.org/10.5500/wjt.v15.i1.99642