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For: Connor KL, O'Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The Future Role of Machine Learning in Clinical Transplantation. Transplantation 2021;105:723-35. [PMID: 32826798 DOI: 10.1097/TP.0000000000003424] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Sabharwal R, Miah SJ. An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain. J Big Data 2022;9. [DOI: 10.1186/s40537-022-00605-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Mekov E, Ilieva V. Machine learning in lung transplantation: where are we? La Presse Médicale 2022. [DOI: 10.1016/j.lpm.2022.104140] [Reference Citation Analysis]
3 Kherabi Y, Messika J, Peiffer‐smadja N. Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future? Transplant Infectious Dis 2022;24. [DOI: 10.1111/tid.13957] [Reference Citation Analysis]
4 Gotlieb N, Azhie A, Sharma D, Spann A, Suo NJ, Tran J, Orchanian-Cheff A, Wang B, Goldenberg A, Chassé M, Cardinal H, Cohen JP, Lodi A, Dieude M, Bhat M. The promise of machine learning applications in solid organ transplantation. NPJ Digit Med 2022;5:89. [PMID: 35817953 DOI: 10.1038/s41746-022-00637-2] [Reference Citation Analysis]
5 Thongprayoon C, Mao SA, Jadlowiec CC, Mao MA, Leeaphorn N, Kaewput W, Vaitla P, Pattharanitima P, Tangpanithandee S, Krisanapan P, Qureshi F, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States. J Clin Med 2022;11:3288. [PMID: 35743357 DOI: 10.3390/jcm11123288] [Reference Citation Analysis]
6 Thongprayoon C, Jadlowiec CC, Kaewput W, Vaitla P, Mao SA, Mao MA, Leeaphorn N, Qureshi F, Pattharanitima P, Qureshi F, Acharya PC, Nissaisorakarn P, Cooper M, Cheungpasitporn W. Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering. JPM 2022;12:859. [DOI: 10.3390/jpm12060859] [Reference Citation Analysis]
7 Taner T, Bruner J, Emamaullee J, Bonaccorsi-Riani E, Zarrinpar A. New Approaches to the Diagnosis of Rejection and Prediction of Tolerance in Liver Transplantation. Transplantation 2022. [PMID: 35594482 DOI: 10.1097/TP.0000000000004160] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
8 Balch JA, Delitto D, Tighe PJ, Zarrinpar A, Efron PA, Rashidi P, Upchurch GR Jr, Bihorac A, Loftus TJ. Machine Learning Applications in Solid Organ Transplantation and Related Complications. Front Immunol 2021;12:739728. [PMID: 34603324 DOI: 10.3389/fimmu.2021.739728] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]