# Extending Machine Learning-Based Early Sepsis Detection to Different Demographics

arXiv: https://arxiv.org/abs/2311.04325 · HTML: https://mlsanity.com/paper/2311.04325/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients](https://mlsanity.com/paper/2306.14016/) (2023)
- [Investigating Poor Performance Regions of Black Boxes: LIME-based Exploration in Sepsis Detection](https://mlsanity.com/paper/2306.12507/) (2023)

## Built on by (most cited first, 1 of 1)

- [Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps](https://mlsanity.com/paper/2405.01714/) (2024)

## Abstract (arXiv)

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.