Machine learning in solid organ transplantation: Charting the evolving landscape

Table 1 Top countries based on the number of publications and citations

Country	Publications (n = 427)	Citations	Percentage from total citations (n = 3979) (%)	Total link strength
United States	209	2288	57.5	137
Canada	59	828	20.81	89
China	43	336	8.44	7
Spain	34	516	12.97	46
England	27	737	18.52	38
France	25	538	13.52	49
Germany	25	649	16.31	37
Italy	20	202	5.08	31
Belgium	16	436	10.96	37
South Korea	15	163	4.1	3
Australia	13	538	13.52	33
Austria	13	607	15.26	47
Thailand	12	21	0.53	12
Brazil	11	50	1.26	7
Netherlands	10	322	8.09	29
India	9	77	1.94	9
Switzerland	9	130	3.27	12
Sweden	8	16	0.4	17
Portugal	6	4	0.1	4
Saudi Arabia	6	42	1.06	8

Table 2 Top contributed institutions

Institution	Country	Number of publications	Percentage (%)	Total citations
University of Toronto	Canada	39	9.13	301
University of California System	United States	34	7.96	263
Mayo Clinic	United States	23	5.39	457
Johns Hopkins University	United States	19	4.45	458
UDICE French Research Universities	France	18	4.22	472
University of Texas System	United States	18	4.22	638
Harvard University	United States	17	3.98	359
Pennsylvania Commonwealth System of Higher Education	United States	15	3.51	396
University of Alberta	Canada	14	3.28	585
University of Pittsburgh	United States	14	3.28	395
Assistance Publique Hopitaux Paris	France	13	3.04	458
Inserm, the French National Institute of Health and Medical Research	France	13	3.04	465
Paris Cité University	France	13	3.04	293
Stanford University	United States	12	2.81	17
Thammasat University	Thailand	12	2.81	23
University of Pennsylvania	United States	12	2.81	79
Medical University of Vienna	Austria	11	2.58	576
University of Mississippi	United States	11	2.58	15
Columbia University	United States	10	2.34	48
Duke University	United States	10	2.34	122

Table 3 Top journals based on the number of publications, n (%)

Journal	Publications (n = 427)
Journal of Heart and Lung Transplantation	47 (11)
American Journal of Transplantation	33 (7.7)
Transplantation	17 (3.9)
Transplant International	12 (2.8)
PLOS One	11 (2.6)
Scientific Reports	11 (2.6)
Hepatology	10 (2.3)
Journal of Clinical Medicine	9 (2.1)
Frontiers in Immunology	7 (1.6)
Lecture Notes in Computer Science	7 (1.6)
Liver Transplantation	7 (1.6)

Table 4 Top contributed authors

Author	Publications	Percentage (%)
Wisit Cheungpasitporn	13	3.04
Charat Thongprayoon	13	3.04
Napat Leeaphorn	12	2.81
Matthew Cooper	11	2.58
Lauren Erdman	11	2.58
Caroline C Jadlowiec	11	2.58
Pattharawin Pattharanitima	11	2.58
Michael A Mao	10	2.34
Shennen A Mao	10	2.34
Gonzalo Sapisochin	10	2.34
Pradeep Vaitla	10	2.34
Wisit Kaewput	9	2.11
Anna Goldenberg	8	1.87
Philip F Halloran	8	1.87
César Hervas-Martinez	8	1.87
Lawrence Lau	8	1.87
Alexandre Loupy	8	1.87
Maarten Naesens	8	1.87
Mamatha Bhat	7	1.64
Javier Briceno	7	1.64
Shaf Keshavjee	7	1.64
Tommy Ivanics	6	1.41
Pajaree Krisanapan	6	1.41
Carmen Lefaucheur	6	1.41
Pitchaphon Nissaisorakarn	6	1.41

Table 5 Top ten cited articles in the field of machine learning in solid organ transplantation

Title	First author	Journal	Publication year	Total citations
Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation	Torgyn Shaikhina	Biomedical Signal Processing and Control	2019	162
Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma	Amit Singal	American Journal of Gastroenterology	2013	152
Prediction of acute kidney injury after liver transplantation: Machine learning approaches vs. logistic regression model	Hyung-Chul Lee	Journal of Clinical Medicine	2018	96
Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes	Jeff Reeve	JCI Insight	2017	90
Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients	Jie Tang	Scientific Reports	2017	83
Predicting the graft survival for heart-lung transplantation patients: An integrated data mining methodology	Asil Oztekin	International Journal of Medical Informatics	2009	79
Machine-learning algorithms predict graft failure after liver transplantation	Lawrence Lau	Transplantation	2017	76
Applying machine learning in liver disease and transplantation: A comprehensive review	Ashley Spann	Hepatology	2020	66
Predicting graft survival among kidney transplant recipients: A Bayesian decision support model	Kazim Topuz	Decision Support Systems	2018	64
Transcriptional trajectories of human kidney injury progression	Pietro Cippa	JCI Insight	2018	58