دورية أكاديمية
أعرض في EDS

The use of machine learning in paediatric nutrition.

التفاصيل البيبلوغرافية
العنوان: The use of machine learning in paediatric nutrition.
المؤلفون: Young A; Southampton Children's Hospital, University Hospital Southampton NHS Foundation Trust.; University of Southampton., Johnson MJ; Southampton Children's Hospital, University Hospital Southampton NHS Foundation Trust.; NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust and University of Southampton, Southampton, UK., Beattie RM; Southampton Children's Hospital, University Hospital Southampton NHS Foundation Trust.
المصدر: Current opinion in clinical nutrition and metabolic care [Curr Opin Clin Nutr Metab Care] 2024 May 01; Vol. 27 (3), pp. 290-296. Date of Electronic Publication: 2024 Jan 31.
نوع المنشور: Review; Journal Article
اللغة: English
بيانات الدورية: Publisher: Lippincott Williams & Wilkins Country of Publication: England NLM ID: 9804399 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1473-6519 (Electronic) Linking ISSN: 13631950 NLM ISO Abbreviation: Curr Opin Clin Nutr Metab Care Subsets: MEDLINE
أسماء مطبوعة: Publication: 1999- : London : Lippincott Williams & Wilkins
Original Publication: London ; Philadelphia : Rapid Science Publishers, c1998-
مواضيع طبية MeSH: Machine Learning* , Child Nutritional Physiological Phenomena*, Child ; Humans ; Obesity
مستخلص: Purpose of Review: In recent years, there has been a burgeoning interest in using machine learning methods. This has been accompanied by an expansion in the availability and ease of use of machine learning tools and an increase in the number of large, complex datasets which are suited to machine learning approaches. This review summarizes recent work in the field and sets expectations for its impact in the future.
Recent Findings: Much work has focused on establishing good practices and ethical frameworks to guide the use of machine learning in research. Machine learning has an established role in identifying features in 'omics' research and is emerging as a tool to generate predictive models to identify people at risk of disease and patients at risk of complications. They have been used to identify risks for malnutrition and obesity. Machine learning techniques have also been used to develop smartphone apps to track behaviour and provide healthcare advice.
Summary: Machine learning techniques are reaching maturity and their impact on observational data analysis and behaviour change will come to fruition in the next 5 years. A set of standards and best practices are emerging and should be implemented by researchers and publishers.
(Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
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تواريخ الأحداث: Date Created: 20240131 Date Completed: 20240405 Latest Revision: 20240531
رمز التحديث: 20240601
DOI: 10.1097/MCO.0000000000001018
PMID: 38294876
قاعدة البيانات: MEDLINE
الوصف
تدمد:1473-6519
DOI:10.1097/MCO.0000000000001018