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Cited by in F6Publishing
For: Luo Y, Tang Z, Hu X, Lu S, Miao B, Hong S, Bai H, Sun C, Qiu J, Liang H, Na N. Machine learning for the prediction of severe pneumonia during posttransplant hospitalization in recipients of a deceased-donor kidney transplant. Ann Transl Med. 2020;8:82. [PMID: 32175375 DOI: 10.21037/atm.2020.01.09] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020;9:E1107. [PMID: 32294906 DOI: 10.3390/jcm9041107] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 9.0] [Reference Citation Analysis]
2 Bülow RD, Dimitrov D, Boor P, Saez-Rodriguez J. How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade? Semin Immunopathol 2021. [PMID: 33835214 DOI: 10.1007/s00281-021-00847-y] [Reference Citation Analysis]
3 Schwantes IR, Axelrod DA. Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation. Curr Transplant Rep 2021;:1-6. [PMID: 34341714 DOI: 10.1007/s40472-021-00336-z] [Reference Citation Analysis]
4 Chen C, Yang D, Gao S, Zhang Y, Chen L, Wang B, Mo Z, Yang Y, Hei Z, Zhou S. Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation. Respir Res 2021;22:94. [PMID: 33789673 DOI: 10.1186/s12931-021-01690-3] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Seyahi N, Ozcan SG. Artificial intelligence and kidney transplantation. World J Transplant 2021; 11(7): 277-289 [PMID: 34316452 DOI: 10.5500/wjt.v11.i7.277] [Reference Citation Analysis]
6 Castillo-astorga R, Sotomayor CG. Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence. Transplantology 2021;2:118-28. [DOI: 10.3390/transplantology2020012] [Reference Citation Analysis]
7 Nakagami G, Yokota S, Kitamura A, Takahashi T, Morita K, Noguchi H, Ohe K, Sanada H. Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan. Int J Nurs Stud 2021;119:103932. [PMID: 33975074 DOI: 10.1016/j.ijnurstu.2021.103932] [Reference Citation Analysis]
8 Guo K, Fu X, Zhang H, Wang M, Hong S, Ma S. Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data. Transl Pediatr 2021;10:33-43. [PMID: 33633935 DOI: 10.21037/tp-20-238] [Reference Citation Analysis]
9 Gupta S, Garg N, Sinha D, Yadav B, Gupta B, Miah S, Khan R. The Emerging Role of Implementing Machine Learning in Food Recommendation for Chronic Kidney Diseases Using Correlation Analysis. Journal of Food Quality 2022;2022:1-10. [DOI: 10.1155/2022/7176261] [Reference Citation Analysis]
10 Qiu Y, Su Y, Tu GW, Ju MJ, He HY, Gu ZY, Yang C, Luo Z. Neutrophil-to-Lymphocyte Ratio Predicts Mortality in Adult Renal Transplant Recipients with Severe Community-Acquired Pneumonia. Pathogens 2020;9:E913. [PMID: 33158161 DOI: 10.3390/pathogens9110913] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]