What question did this study set out to answer?

The review aims to assess the effectiveness and quality of machine learning models in predicting sepsis.

April 10, 2026

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

Key Points

The review aims to assess the effectiveness and quality of machine learning models in predicting sepsis.
Conducted a systematic review of existing literature on machine learning models for sepsis prediction.
Evaluated the quality and robustness of the methodologies used in the studies.
Identified areas for improvement in data integrity and algorithm development.
Found significant variability in study quality and methodology.
Highlighted the need for enhanced data quality to improve prediction accuracy.
Suggested the necessity of multi-center prospective studies for better model validation.

Abstract

Machine learning models hold promise for accurately predicting sepsis, but study quality varies, with notable flaws in methodology, robustness, and reproducibility. Future research should enhance data quality, advance algorithms, and perform multi-center prospective studies for validation.

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Authors

Tingrui Wang

Qinqin Li

Zhangyi Wang

Journals

Shock

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Institutions

Lanzhou University

Guizhou University

Lanzhou University of Technology

References and Citations

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Building similarity graph...

Analyzing shared references across papers

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

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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