Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.

ABSTRACT:

Background

With machine learning (ML) carving a niche in diverse medical disciplines, its role in sepsis prediction, a condition where the 'golden hour' is critical, is of paramount interest. This study assesses the factors influencing the efficacy of ML models in sepsis prediction, aiming to optimize their use in clinical practice.

Methods

We searched Medline, PubMed, Google Scholar, and CENTRAL for studies published from inception to October 2023. We focused on studies predicting sepsis in real-time settings in adult patients in any hospital settings without language limits. The primary outcome was area under the curve (AUC) of the receiver operating characteristic. This meta-analysis was conducted according to PRISMA-NMA guidelines and Cochrane Handbook recommendations. A

SUBMITTER: Yadgarov MY 

PROVIDER: S-EPMC11523135 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature