Extending Machine Learning-Based Early Sepsis Detection to Different Demographics

• URL: http://arxiv.org/abs/2311.04325v1
• Date: Tue, 7 Nov 2023 20:02:52 GMT
• Title: Extending Machine Learning-Based Early Sepsis Detection to Different Demographics
• Authors: Surajsinh Parmar and Tao Shan and San Lee and Yonghwan Kim and Jang Yong Kim
• Abstract summary: We compare 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.
• Score: 1.2724528787590168
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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