Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.

ABSTRACT:

Purpose

Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.

Methods

A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Mod

SUBMITTER: Fleuren LM 

PROVIDER: S-EPMC7067741 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature