For: | Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9(36): 11255-11264 [PMID: 35071556 DOI: 10.12998/wjcc.v9.i36.11255] |
---|---|
URL: | https://www.wjgnet.com/2307-8960/full/v9/i36/11255.htm |
Number | Citing Articles |
1 |
Jane Wang, Francesca Tozzi, Amir Ashraf Ganjouei, Fernanda Romero-Hernandez, Jean Feng, Lucia Calthorpe, Maria Castro, Greta Davis, Jacquelyn Withers, Connie Zhou, Zaim Chaudhary, Mohamed Adam, Frederik Berrevoet, Adnan Alseidi, Nikdokht Rashidian. Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis. Journal of Gastrointestinal Surgery 2024; 28(6): 956 doi: 10.1016/j.gassur.2024.03.006
|
2 |
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
|
3 |
Rafael Calleja, Manuel Durán, María Dolores Ayllón, Ruben Ciria, Javier Briceño. Machine learning in liver surgery: Benefits and pitfalls. World Journal of Clinical Cases 2024; 12(12): 2134-2137 doi: 10.12998/wjcc.v12.i12.2134
|
4 |
Tingting Fan, Jiaxin Wang, Luyao Li, Jing Kang, Wenrui Wang, Chuan Zhang. Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost. Frontiers in Public Health 2023; 11 doi: 10.3389/fpubh.2023.1087297
|
5 |
Wisit Cheungpasitporn, Charat Thongprayoon, Kianoush B. Kashani. Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury. Kidney Research and Clinical Practice 2024; 43(4): 417 doi: 10.23876/j.krcp.23.298
|