Enhancing sepsis management through machine learning techniques: A review

Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative...

Full description

Saved in:
Bibliographic Details
Published in:Medicina intensiva 2022-03, Vol.46 (3), p.140-156
Main Authors: Ocampo-Quintero, N, Vidal-Cortés, P, Del Río Carbajo, L, Fdez-Riverola, F, Reboiro-Jato, M, Glez-Peña, D
Format: Article
Language:English
Subjects:
Clinical Decision-Making
Humans
Machine Learning
Sepsis - diagnosis
Sepsis - therapy
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic prediction of diverse patient conditions, as well as assistance in clinical decision making. In this context, this work conducts a narrative review to provide an overview of how specific Machine Learning techniques can be used to improve sepsis management, discussing the main tasks addressed, the most popular methods and techniques, as well as the obtained results, in terms of both intelligent system accuracy and clinical outcomes improvement.
ISSN:2173-5727
2173-5727
1578-6749
DOI:10.1016/j.medine.2020.04.015