Copyright
©The Author(s) 2024.
World J Hepatol. Apr 27, 2024; 16(4): 625-639
Published online Apr 27, 2024. doi: 10.4254/wjh.v16.i4.625
Published online Apr 27, 2024. doi: 10.4254/wjh.v16.i4.625
Figure 2 Clinical feature selection based on least absolute shrinkage and selection operator logistic regression.
A: Selection of the optimal lambda according to least absolute shrinkage and selection operator (LASSO) logistic regression. Each line represents the change in the coefficient of each feature; B: LASSO coefficient profiles of features. The left and right black vertical lines were drawn at the lambda with minim deviance and 1 standard error to the lambda with minim deviance.
- Citation: Tang XW, Ren WS, Huang S, Zou K, Xu H, Shi XM, Zhang W, Shi L, Lü MH. Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis. World J Hepatol 2024; 16(4): 625-639
- URL: https://www.wjgnet.com/1948-5182/full/v16/i4/625.htm
- DOI: https://dx.doi.org/10.4254/wjh.v16.i4.625