BPG is committed to discovery and dissemination of knowledge
Cited by in CrossRef
For: Bredt LC, Peres LAB, Risso M, Barros LCAL. Risk factors and prediction of acute kidney injury after liver transplantation: Logistic regression and artificial neural network approaches . World J Hepatol 2022; 14(3): 570-582 [PMID: 35582300 DOI: 10.4254/wjh.v14.i3.570]
URL: https://www.wjgnet.com/1948-5182/full/v14/i3/570.htm
Number Citing Articles
1
Gidion Chongo, Jonathan Soldera. Use of machine learning models for the prognostication of liver transplantation: A systematic reviewWorld Journal of Transplantation 2024; 14(1): 88891 doi: 10.5500/wjt.v14.i1.88891
2
Nam-Jun Cho, Inyong Jeong, Se-Jin Ahn, Hyo-Wook Gil, Yeongmin Kim, Jin-Hyun Park, Sanghee Kang, Hwamin Lee. Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective StudyJournal of Medical Internet Research 2025; 27: e66568 doi: 10.2196/66568
3
Jason Mann, Mathew Lyons, John O'Rourke, Simon Davies. Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative reviewJournal of Clinical Anesthesia 2025; 102: 111782 doi: 10.1016/j.jclinane.2025.111782
4
Allan R.M. França, Eduardo Rocha, Leonardo S.L. Bastos, Fernando A. Bozza, Pedro Kurtz, Elizabeth Maccariello, José Roberto Lapa e Silva, Jorge I.F. Salluh. Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19Journal of Critical Care 2024; 80: 154480 doi: 10.1016/j.jcrc.2023.154480
5
Parichat Tovikkai, Suvit Soontarinka, Manee Raksakietisak, Chutwichai Tovikkai, Orawan Pongraweewan, Arunotai Siriussawakul, Kittiphong Sujirattanawimol, Annop Piriyapatsom, Rattanaporn Tankul, Nattachai Hemtanon, Chularat Noinonthong, Chumsab Rattanaruangrit, Sutatta Boonyakarn, Aphichat Suphathamwit. The association of intraoperative hypotension during orthotopic liver transplantation and post-transplant outcomesAsian Journal of Surgery 2025; 48(3): 1666 doi: 10.1016/j.asjsur.2024.11.213
6
Jingying Huang, Jiaojiao Chen, Jin Yang, Mengbo Han, Zihao Xue, Yina Wang, Miaomiao Xu, Haiou Qi, Yuting Wang. Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisalIntensive and Critical Care Nursing 2025; 86: 103808 doi: 10.1016/j.iccn.2024.103808
7
Taiyo Kuroda, Barry D. Kuban, Takuma Miyamoto, Chihiro Miyagi, Anthony R. Polakowski, Christine R. Flick, Jamshid H. Karimov, Kiyotaka Fukamachi. Artificial Deep Neural Network for Sensorless Pump Flow and Hemodynamics Estimation During Continuous-Flow Mechanical Circulatory SupportASAIO Journal 2023; 69(7): 649 doi: 10.1097/MAT.0000000000001926
8
Yordan Penev, Matthew M. Ruppert, Ahmet Bilgili, Youlei Li, Raiya Habib, Abdul-Vehab Dozic, Coulter Small, Esra Adiyeke, Tezcan Ozrazgat-Baslanti, Tyler J. Loftus, Chris Giordano, Azra Bihorac. Intraoperative hypotension and postoperative acute kidney injury: A systematic reviewThe American Journal of Surgery 2024; 232: 45 doi: 10.1016/j.amjsurg.2024.02.001
9
Xiang Yu, Yuwei Ji, Mengjie Huang, Zhe Feng. Machine learning for acute kidney injury: Changing the traditional disease prediction modeFrontiers in Medicine 2023; 10 doi: 10.3389/fmed.2023.1050255
10
Raja Al-Bahou, Julia Bruner, Helen Moore, Ali Zarrinpar. Quantitative methods for optimizing patient outcomes in liver transplantationLiver Transplantation 2023;  doi: 10.1097/LVT.0000000000000325
11
Feifei Lu, Yao Meng, Xiaoting Song, Xiaotong Li, Zhuang Liu, Chunru Gu, Xiaojie Zheng, Yi Jing, Wei Cai, Kanokwan Pinyopornpanish, Andrea Mancuso, Fernando Gomes Romeiro, Nahum Méndez-Sánchez, Xingshun Qi. Artificial Intelligence in Liver Diseases: Recent AdvancesAdvances in Therapy 2024; 41(3): 967 doi: 10.1007/s12325-024-02781-5