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©The Author(s) 2022.
World J Gastroenterol. Aug 28, 2022; 28(32): 4681-4697
Published online Aug 28, 2022. doi: 10.3748/wjg.v28.i32.4681
Published online Aug 28, 2022. doi: 10.3748/wjg.v28.i32.4681
Figure 5 Nomogram for the generalized linear model and weighting of variables.
A: Nomogram for the generalized linear model (GLM); B: Intersection variables among the GLM, least absolute shrinkage and selection operator model (LSM), and random forest model (RFM); C: Weights of the intersection variables in the GLM, LSM, and RFM, respectively. SPT: Spleen thickness; L: Lymphocyte count; DPV: Diameter of portal vein; PPER1 and PPER3: The first and third days for postoperative platelet elevation rate; PVT: Portal vein thrombosis; PLR: Platelet to lymphocyte ratio; NLR: Neutrophil to lymphocyte ratio; PLT: Platelet count; PTA: Prothrombin activity; EGV: Esophageal and gastric varices; AST: Aspartate aminotransaminase.
- Citation: Li J, Wu QQ, Zhu RH, Lv X, Wang WQ, Wang JL, Liang BY, Huang ZY, Zhang EL. Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models. World J Gastroenterol 2022; 28(32): 4681-4697
- URL: https://www.wjgnet.com/1007-9327/full/v28/i32/4681.htm
- DOI: https://dx.doi.org/10.3748/wjg.v28.i32.4681