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...

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Bibliographic Details
Published in:Medicina intensiva (English ed.) 2020-05
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
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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