Improving Sepsis Mortality Prediction With Machine Learning Using Full Region Synthetic Sampling Approach.

ABSTRACT:

Background and aims

Sepsis is a life-threatening condition and remains a leading cause of mortality in Intensive Care Units (ICUs). Accurate mortality prediction is crucial for optimizing ICU resource allocation. However, severe class imbalance in ICU datasets hampers the generalization performance of machine learning models.

Methods

This study proposes a Full Region Synthetic Sampling Approach (FRSSA), a novel data augmentation method that dynamically balances the minority class distribution based on regional density. Additionally, we introduce Adaptive Synthetic Sampling Tuning (ASST), an optimization-based strategy that adjusts augmentation weights to enhance model performance. To evaluate model fairness and clinical utility, we propose the Balanced Performance Score (BPS

SUBMITTER: Amory IA 

PROVIDER: S-EPMC12641153 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature