Academic Content and Language Evaluation of This Article
CrossCheck and Google Search of This Article
Academic Rules and Norms of This Article
Citation of this article
Corresponding Author of This Article
Research Domain of This Article
Article-Type of This Article
Open-Access Policy of This Article
Times Cited Counts in Google of This Article
Number of Hits and Downloads for This Article
- Total Article Views (4137)
All Articles published online
|
Featured Article
|
Publishing Process of This Article
|
Mar 27, 2022 (publication date) through May 5, 2024
Times Cited of This Article
Journal Information of This Article
Publication Name
World Journal of Hepatology
ISSN
1948-5182
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
| 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 review. World Journal of Transplantation 2024; 14(1): 88891 doi: 10.5500/wjt.v14.i1.88891
Abstract(114) |
Core Tip(104) |
Full Article(HTML)(678)
|
Full Article with Cover (PDF)-2751K(20)
|
Audio-116K(0)
|
Peer-Review Report-216K(10)
|
Answering Reviewers-76K(12)
|
Supplementary Material-109K(12)
|
Full Article (PDF)-2406K(43)
|
Full Article (XML)-310K(15)
|
Times Cited (0)
|
Total Visits (1453)
|
Open
|
| 2 |
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-19. Journal of Critical Care 2024; 80: 154480 doi: 10.1016/j.jcrc.2023.154480
|
| 3 |
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 Support. ASAIO Journal 2023; 69(7): 649 doi: 10.1097/MAT.0000000000001926
|
| 4 |
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 review. The American Journal of Surgery 2024; doi: 10.1016/j.amjsurg.2024.02.001
|
| 5 |
Xiang Yu, Yuwei Ji, Mengjie Huang, Zhe Feng. Machine learning for acute kidney injury: Changing the traditional disease prediction mode. Frontiers in Medicine 2023; 10 doi: 10.3389/fmed.2023.1050255
|
| 6 |
Raja Al-Bahou, Julia Bruner, Helen Moore, Ali Zarrinpar. Quantitative methods for optimizing patient outcomes in liver transplantation. Liver Transplantation 2024; 30(3): 311 doi: 10.1097/LVT.0000000000000325
|
| 7 |
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 Advances. Advances in Therapy 2024; 41(3): 967 doi: 10.1007/s12325-024-02781-5
|