Extending Machine Learning-Based Early Sepsis Detection to Different Demographics

2023/11/07 by Surajsinh Parmar, Tao Shan, Parmar, Surajsinh +7
Computer Science · Health Professions · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Sepsis Diagnosis and Treatment

paper · pdf · doi:10.48550/arxiv.2311.04325

openalex publication_date 2023/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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