• Medicina intensiva · May 2020

    Review

    Enhancing sepsis management through machine learning techniques: A review.

    • N Ocampo-Quintero, P Vidal-Cortés, L Del Río Carbajo, F Fdez-Riverola, M Reboiro-Jato, and D Glez-Peña.
    • ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain.
    • Med Intensiva. 2020 May 29.

    AbstractSepsis 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.Copyright © 2020 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.

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