Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.

ABSTRACT: Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data Sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study Eligibility C

SUBMITTER: Moor M 

PROVIDER: S-EPMC8193357 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature