Extending Machine Learning-Based Early Sepsis Detection to Different Demographics
This work addresses sepsis detection for diverse demographics, but it is incremental as it applies existing methods to new data.
The study compared LightGBM and XGBoost for sepsis detection using Western and South Korean datasets, finding LightGBM slightly more efficient and scalable.
Sepsis requires urgent diagnosis, but research is predominantly focused on Western datasets. In this study, we perform a comparative analysis of two ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis reveals the effectiveness of these methods in addressing healthcare data imbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight edge in computational efficiency and scalability. The study paves the way for the broader application of machine learning in critical care, thereby expanding the reach of predictive analytics in healthcare globally.