Flowchart of patient inclusion.

<div><p>Background</p><p>Lymphoma is a severe condition with high mortality rates, often requiring ICU admission. Traditional risk stratification tools like SOFA and APACHE scores struggle to capture complex clinical interactions. Machine learning (ML) models offer a more acc...

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Bibliographic Details
Main Author:	Ling Xu (117663) (author)
Other Authors:	Guang Tu (22054865) (author), Zhonglan Cai (22054874) (author), Tianbi Lan (9280010) (author)
Published:	2025
Subjects:	Medicine Biotechnology Developmental Biology Cancer Virology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified systolic blood pressure shapley additive explanation provide valuable insights medical information mart apache scores struggle analyzing large datasets model &# 8217 including logistic regression highest predictive performance lymphoma using data improve patient outcomes intensive care iv catboost classifier demonstrated adult patients admitted xlink "> lymphoma shap analysis highlighted high mortality rates bun ), platelets xlink "> model performance catboost classifier lasso regression clinical outcomes study aims severe condition risk stratification random forest primary diagnosis neural networks mortality prediction models offer machine learning laboratory parameters important factor identify high hospital mortality hospital deaths heart rate gradient boosting future work critical role clinical implementation clinical decision baseline characteristics accurate alternative 5 %) 1591 patients
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Flowchart of patient inclusion.

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