Gastrointestinal failure, big data and intensive care.
Journal
Current opinion in clinical nutrition and metabolic care
ISSN: 1473-6519
Titre abrégé: Curr Opin Clin Nutr Metab Care
Pays: England
ID NLM: 9804399
Informations de publication
Date de publication:
01 09 2023
01 09 2023
Historique:
medline:
4
8
2023
pubmed:
30
6
2023
entrez:
30
6
2023
Statut:
ppublish
Résumé
Enteral feeding is the main route of administration of medical nutritional therapy in the critically ill. However, its failure is associated with increased complications. Machine learning and artificial intelligence have been used in intensive care to predict complications. The aim of this review is to explore the ability of machine learning to support decision making to ensure successful nutritional therapy. Numerous conditions such as sepsis, acute kidney injury or indication for mechanical ventilation can be predicted using machine learning. Recently, machine learning has been applied to explore how gastrointestinal symptoms in addition to demographic parameters and severity scores, can accurately predict outcomes and successful administration of medical nutritional therapy. With the rise of precision and personalized medicine for support of medical decisions, machine learning is gaining popularity in the field of intensive care, first not only to predict acute renal failure or indication for intubation but also to define the best parameters for recognizing gastrointestinal intolerance and to recognize patients intolerant to enteral feeding. Large data availability and improvement in data science will make machine learning an important tool to improve medical nutritional therapy.
Identifiants
pubmed: 37389458
doi: 10.1097/MCO.0000000000000961
pii: 00075197-990000000-00093
doi:
Types de publication
Review
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
476-481Informations de copyright
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
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