| 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) 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 |
Javier Briceño, Ruben Ciria, María Dolores Ayllón, Manuel Durán, Rafael Calleja. 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 |
Ala Nozari, Dhanesh D. Binda, Maxwell B. Baker. Advancements in Neuroanesthesia Through Artificial Intelligence. Anesthesiology Clinics 2025; 43(3) doi: 10.1016/j.anclin.2025.05.006
|
| 5 |
Yu Guan, Jing Zhu, Hong Zhu, Siyuan Zhang, Liping Wu, Wenjie Lin. Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy. International Ophthalmology 2026; 46(1) doi: 10.1007/s10792-026-04168-7
|
| 6 |
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
|
| 7 |
Masoumeh Kheirkhahzadeh, Amin Golabpour. The Role of Artificial Intelligence in Predicting Acute Kidney Injury: A
Systematic Review of Machine Learning and Deep Learning Approaches. North Khorasan University of Medical Sciences 2026; 18(1) doi: 10.66224/nkums.18.1.91
|
| 8 |
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) doi: 10.23876/j.krcp.23.298
|
| 9 |
Inyong Jeong, Nam-Jun Cho, Se-Jin Ahn, Hwamin Lee, Hyo-Wook Gil. Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions. The Korean Journal of Internal Medicine 2024; 39(6) doi: 10.3904/kjim.2024.098
|