Performance of each model for prediction.

Background
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 accurate alternative for predicting outcomes by analyzing large datasets. However, their application in predicting in-hospital mortality for lymphoma patients remains limited.
Objective
This study aims to develop and validate machine learning models to predict in-hospital mortality in ICU patients with lymphoma using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, thereby enhancing risk stratification and clinical decision-making.
Methods
We conducted a retrospective cohort study using data from the MIMIC-IV database, which includes detailed clinical data from adult patients admitted to the ICU. Patients with a primary diagnosis of lymphoma were included. Baseline characteristics, laboratory parameters, and clinical outcomes were extracted. Lasso regression was employed to screen for significant risk factors associated with in-hospital mortality. Fifteen machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. Model performance was evaluated through cross-validation and SHapley Additive exPlanation (SHAP) values to interpret variable importance.
Results
A total of 1591 patients were included, with 342 (21.5%) in-hospital deaths. Lasso regression identified significant predictors of mortality, including blood urea nitrogen (BUN), platelets, PT, heart rate, systolic blood pressure, APTT, spo2, and bicarbonate. The CatBoost Classifier demonstrated the highest predictive performance with an AUC of 0.7766. SHAP analysis highlighted the critical role of BUN as the most important factor in mortality prediction, followed by platelets and PT. The SHAP force plot provided individualized risk assessments for patients, demonstrating the model’s ability to identify high-risk subgroups.
Conclusion
Machine learning models, particularly the CatBoost Classifier, effectively predict in-hospital mortality in ICU patients with lymphoma. These models outperform traditional statistical methods and provide valuable insights into risk stratification. Future work should focus on external validation and clinical implementation to improve patient outcomes in this high-risk population.