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Updated: Oct 22, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

More Generalizable Models For Sepsis Detection Under Covariate Shift.

Jifan Gao¹, Philip L Mar², Guanhua Chen¹

¹University of Wisconsin, School of Medicine and Public Health.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|August 30, 2021

Summary

This summary is machine-generated.

Machine learning models for sepsis detection can fail when data distributions change. This study shows that covariate shift corrections improve model generalizability for early sepsis recognition in intensive care units (ICUs).

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A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

Area of Science:

Critical Care Medicine
Biomedical Data Science
Machine Learning in Healthcare

Background:

Sepsis is a leading cause of mortality in intensive care units (ICUs).
Early sepsis intervention improves patient outcomes.
Supervised machine learning models for sepsis detection assume consistent data distributions between training and testing phases.

Purpose of the Study:

To investigate the impact of covariate shift on machine learning models for sepsis detection.
To evaluate the effectiveness of covariate shift corrections in enhancing model generalizability.

Main Methods:

Applied covariate shift correction techniques to standard machine learning models.
Assessed model performance under conditions of covariate shift.

Main Results:

Observed that covariate shift can degrade the performance of machine learning models in sepsis detection.
Demonstrated that applying covariate shift corrections improves model generalizability.

Conclusions:

Covariate shift is a significant challenge for clinical risk prediction models, including those for sepsis.
Covariate shift corrections are a viable strategy to enhance the robustness and generalizability of machine learning models for detecting sepsis onset.