Permutation importance (a, c) and SHAP summary plots (b, d) for the Gradient Boosting Classifier and Random Forest Classifier, showing variable associations with in-hospital mortality.

Permutation importance (a, c) and SHAP summary plots (b, d) for the Gradient Boosting Classifier and Random Forest Classifier, showing variable associations with in-hospital mortality.

<p>Permutation importance (a, c) and SHAP summary plots (b, d) for the Gradient Boosting Classifier and Random Forest Classifier, showing variable associations with in-hospital mortality.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Guang Tu (22054865) (author)
مؤلفون آخرون:	Zhonglan Cai (22054874) (author), Ling Wu (151826) (author), Hang Yu (278278) (author), Hongke Jiang (21596432) (author), Haijian Luo (22057247) (author)
منشور في:	2025
الموضوعات:	Medicine Biotechnology Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified top 13 variables model &# 8217 intensive care units including logistic regression extracted baseline characteristics explain variable importance demonstrated superior performance coronary heart disease blood urea nitrogen analyze large datasets 6 %) experienced identify intricate patterns study included 2 gradient boosting classifier improve clinical outcomes adult icu patients accurately identify high hospital mortality rates gradient boosting clinical outcomes icu patients identify high study aims hospital mortality clinical implementation risk patients 213 patients xlink "> thereby providing significantly contribute risk stratification random forest promising alternative primary diagnosis practical tool neural networks machine learning laboratory parameters iv database important predictors highly prevalent future work feature selection external validation boruta algorithm
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