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Enhancing sepsis management through machine learning techniques: A review.
Ocampo-Quintero, N; Vidal-Cortés, P; Del Río Carbajo, L; Fdez-Riverola, F; Reboiro-Jato, M; Glez-Peña, D.
Afiliación
  • Ocampo-Quintero N; ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain.
  • Vidal-Cortés P; Intensive Care Unit, Complexo Hospitalario Universitario de Ourense, Ourense, Spain.
  • Del Río Carbajo L; Intensive Care Unit, Complexo Hospitalario Universitario de Ourense, Ourense, Spain.
  • Fdez-Riverola F; ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain; CINBIO - Centro de Investigaciones Biomédicas, Universidad de Vigo, Vigo, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain.
  • Reboiro-Jato M; ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain; CINBIO - Centro de Investigaciones Biomédicas, Universidad de Vigo, Vigo, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain.
  • Glez-Peña D; ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain; CINBIO - Centro de Investigaciones Biomédicas, Universidad de Vigo, Vigo, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain. Electronic address: dgpena@uvigo
Med Intensiva (Engl Ed) ; 2020 May 29.
Article en En, Es | MEDLINE | ID: mdl-32482370
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En / Es Revista: Med Intensiva (Engl Ed) Año: 2020 Tipo del documento: Article País de afiliación: España Pais de publicación: España
Texto completo
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Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En / Es Revista: Med Intensiva (Engl Ed) Año: 2020 Tipo del documento: Article País de afiliación: España Pais de publicación: España