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Related Experiment Video

Updated: Apr 9, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

584

A Systematic Review Of Machine Learning Models For Sepsis Prediction: An Appraise-Ai Approach.

Tingrui Wang1, Qinqin Li1, Zhangyi Wang2

  • 1School of Nursing, Guizhou Medical University, Guizhou,China.

Shock (Augusta, Ga.)
|April 8, 2026
PubMed
Summary

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Machine learning (ML) models show promise for early sepsis detection, outperforming traditional methods. However, current research quality varies, necessitating improvements in data and methodology for reliable clinical application.

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Biomedical Data Science

Background:

  • Sepsis is a major cause of intensive care unit mortality and significant healthcare expenditure.
  • Early sepsis detection remains a critical clinical challenge.
  • Machine learning (ML) offers a potential solution for real-time sepsis prediction by analyzing complex patient data.

Purpose of the Study:

  • To assess the effectiveness of ML models in sepsis prediction.
  • To evaluate the methodological and reporting standards of existing ML sepsis research.
  • To determine the accuracy, sensitivity, specificity, and clinical utility of these models.

Main Methods:

  • A systematic review of randomized controlled trials, cohort studies, and nested case-control studies using ML for sepsis prediction.
Keywords:
Early detectionMachine LearningOnset predictionSepsisSystematic review

Related Experiment Videos

Last Updated: Apr 9, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

584
  • Data extraction and quality assessment using the APPRAISE-AI tool across six key domains.
  • Evaluation of model performance metrics including AUC, sensitivity, and specificity.

    • Main Results:

    • The review encompassed 53 studies, predominantly retrospective cohort studies published between 2020 and 2024.
    • Most studies exhibited moderate quality according to the APPRAISE-AI assessment, with significant variability.
    • Area Under the Curve (AUC) values ranged from 0.64 to 0.98, with Light GBM and Multilayer Perceptron (MLP) models achieving the highest performance (0.98).
    • AI-driven models generally demonstrated superior performance compared to non-AI methods for sepsis prediction.

    Conclusions:

    • ML models demonstrate considerable potential for accurate sepsis prediction.
    • Existing studies often suffer from methodological limitations, impacting result robustness and reproducibility.
    • Future research should prioritize enhancing data quality, refining algorithms, and conducting multi-center prospective validation studies.