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.

翻译：脓毒症需要紧急诊断，但目前研究主要集中于西方数据集。本研究使用公开的eICU-CRD数据集和韩国圣玛丽医院私有数据集，对两种集成学习方法（LightGBM和XGBoost）进行了比较分析。分析揭示了这些方法在解决医疗数据不平衡问题和提升脓毒症检测效能方面的有效性。具体而言，LightGBM在计算效率和可扩展性方面略占优势。本研究为机器学习在重症监护中的广泛应用奠定了基础，从而将预测性分析在全球医疗保健领域的应用范围进一步扩大。