Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.

ABSTRACT:

Background

Sepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and economic burden worldwide due to its high mortality rate. Early recognition of sepsis is crucial for effective treatment. This study aimed to systematically evaluate the performance of various machine learning models in predicting the onset of sepsis.

Methods

We conducted a comprehensive search of the Cochrane Library, PubMed, Embase, and Web of Science databases, covering studies from database inception to November 14, 2022. We used the PROBAST tool to assess the risk of bias. We calculated the predictive performance for sepsis onset using the C-index and accuracy. We followed the PRISMA guidelines for this study.

Results

We i

SUBMITTER: Yang Z 

PROVIDER: S-EPMC10523763 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature