| For: | Tian Y, Zhou HY, Liu ML, Ruan Y, Yan ZX, Hu XH, Du J. Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk. World J Gastroenterol 2025; 31(27): 108200 [PMID: 40741101 DOI: 10.3748/wjg.v31.i27.108200] |
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| URL: | https://www.wjgnet.com/1007-9327/full/v31/i27/108200.htm |
| Number | Citing Articles |
| 1 |
Deepika P, Anand L. Multi-model Approach for Investigating the Gut Microbiota Signatures in Hepatic Steatosis Progression. 2025 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) 2025; doi: 10.1109/ICMLANT68509.2025.11394743
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| 2 |
Ottavia Cicerone, Marcello Maestri. Machine learning to predict metabolic-associated fatty liver disease. World Journal of Gastroenterology 2025; 31(45): 114413 doi: 10.3748/wjg.v31.i45.114413
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| 3 |
Nan Xu, Kaiyuan Zhang, Jiao Tian. An Explainable Transformer‐Based Model for Predicting Chronic Diseases Risk. Concurrency and Computation: Practice and Experience 2026; 38(11) doi: 10.1002/cpe.70739
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| 4 |
Zhenyan Lu, Chunqiao He, Mengyuan Wang, Wei He, Shuang Wang, Ying Xie, Xue Pan, Suraiya Saleem. Machine Learning and SHAP Value Interpretation for Predicting Hepatic Steatosis Using Vibration‐Controlled Transient Elastography. International Journal of Endocrinology 2026; 2026(1) doi: 10.1155/ije/3395722
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| 5 |
Yan-Chun Guo, Ye Hong, Li Huang, Xiao-Wei Xu, Jing-Qi Sun, Kang-Kang Ji, Chao-Nian Li. Beyond biomarkers: An integrated traditional Chinese medicine-machine learning approach predicts hepatic steatosis in high metabolic risk populations. World Journal of Gastroenterology 2025; 31(38): 112166 doi: 10.3748/wjg.v31.i38.112166
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