Adaptive weighted stacking model with optimal weights selection for mortality risk prediction in sepsis patients

Abstract

Sepsis patients in the ICU face heightened mortality risks. There still exist challenges that hinder the development of mortality risk prediction models for sepsis patients. In the ensemble model, the differences between base classifier performance can affect the model accuracy and efficiency, and overlapping sample training will lead to repetitive learning, which reduces the model generalization. To tackle these challenges, we propose an Adaptive Weighted Stacking based on Optimal Weights Selection (AWS-OWS) model. A random sampling without replacement is employed to prevent repetitive learning in base classifiers. Additionally, a weighted function and the gradient descent algorithm is adopted to select optimal weights for base classifiers, enhancing the performance of stacking model. The MIMIC-IV dataset is used for model training and internal testing, and the independent samples from MIMIC-III are used for external validation. The results show that AWS-OWS achieves the best AUC of 0.88 in the internal test, with a threefold reduction in computation time compared to standard stacking. In external validation, it also demonstrates good model generalization. AWS-OWS significantly improves the prediction performance and model efficiency, facilitates the identification of high-risk patients with sepsis and supports clinicians in determining appropriate management and treatment strategies.