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Dec 26, 2021 (publication date) through Nov 21, 2024
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Publication Name
World Journal of Clinical Cases
ISSN
2307-8960
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
| 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] |
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| URL: | https://www.wjgnet.com/2307-8960/full/v9/i36/11255.htm |
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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
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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
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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
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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
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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): 882 doi: 10.3904/kjim.2024.098
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