دورية أكاديمية
The use of machine learning in paediatric nutrition.
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.) |
References: | Ashton JJ, Young A, Johnson MJ, Beattie RM. Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities. Pediatr Res 2023; 93:324–333. Thomas DM, Kleinberg S, Brown AW, et al. Machine learning modeling practices to support the principles of AI and ethics in nutrition research. Nutr Diabetes 2022; 12:48. Park S, Kang S. Association of pooled fecal microbiota on height growth in children according to enterotypes. J Pediatr Gastroenterol Nutr 2023; 77:801–810. Robertson RC, Edens TJ, Carr L, et al. The gut microbiome and early-life growth in a population with high prevalence of stunting. Nat Commun 2023; 14:654. Ramírez-Carrillo E, G-Santoyo I, López-Corona O, et al. Similar connectivity of gut microbiota and brain activity networks is mediated by animal protein and lipid intake in children from a Mexican indigenous population. PLoS One 2023; 18:e0281385. Midya V, Lane JM, Gennings C, et al. Prenatal lead exposure is associated with reduced abundance of beneficial gut microbial cliques in late childhood: an investigation using microbial co-occurrence analysis (MiCA). Environ Sci Technol 2023; 57:16800–16810. Torres-Martos Á, Bustos-Aibar M, Ramírez-Mena A, et al. Omics data preprocessing for machine learning: a case study in childhood obesity. Genes (Basel) 2023; 14:248. Li K, Bertrand K, Naviaux JC, et al. Metabolomic and exposomic biomarkers of risk of future neurodevelopmental delay in human milk. Pediatr Res 2023; 93:1710–1720. Moutinho TJ, Powers DA, Hanson GF, et al. Fecal sphingolipids predict parenteral nutrition-associated cholestasis in the neonatal intensive care unit. JPEN J Parenter Enteral Nutr 2022; 46:1903–1913. Shen H, Zhao H, Jiang Y. Machine learning algorithms for predicting stunting among under-five children in Papua New Guinea. Children (Basel) 2023; 10:1638. Khan MNA, Yunus RM. A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries. Nutrition 2023; 108:111947. Nel S, Feucht UD, Nel AL, et al. A novel screening tool to predict severe acute malnutrition through automated monitoring of weight-for-age growth curves. Matern Child Nutr 2022; 18:e13364. Nobre IG, Jurema Santos GC, Santos de Almeida Oliveira TLP, et al. Food consumption habits, gestational age and birth weight are predictive for children with excess weight: an analysis based on artificial neural network. Nutr Health 2022; doi: 10.1177/02601060221124040. (PMID: 10.1177/02601060221124040) Massara P, Lopez-Dominguez L, Bourdon C, et al. A novel systematic pipeline for increased predictability and explainability of growth patterns in children using trajectory features. Int J Med Inform 2023; 177:105143. Krug I, Linardon J, Greenwood C, et al. A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multicentre European project on healthy eating. Psychol Med 2023; 53:2913–2922. Elbassuoni S, Ghattas H, El Ati J, et al. Capturing children food exposure using wearable cameras and deep learning. PLOS Digit Health 2023; 2:e0000211. Folson GK, Bannerman B, Atadze V, et al. Validation of mobile artificial intelligence technology-assisted dietary assessment tool against weighed records and 24-hour recall in adolescent females in Ghana. J Nutr 2023; 153:2328–2338. Nguyen PH, Tran LM, Hoang NT, et al. Relative validity of a mobile AI-technology-assisted dietary assessment in adolescent females in Vietnam. Am J Clin Nutr 2022; 116:992–1001. Hu G, Ahmed M, L’Abbé MR. Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods. Am J Clin Nutr 2023; 117:553–563. Neves PA, Simões J, Costa R, et al. Thought on food: a systematic review of current approaches and challenges for food intake detection. Sensors (Basel) 2022; 22:. Marqués-Sánchez P, Martínez-Fernández MC, Benítez-Andrades JA, et al. Adolescent relational behaviour and the obesity pandemic: a descriptive study applying social network analysis and machine learning techniques. PLoS One 2023; 18:e0289553. Jagarapu J, Diaz MI, Lehmann CU, Medford RJ. Twitter discussions on breastfeeding during the COVID-19 pandemic. Int Breastfeed J 2023; 18:56. Mane HY, Channell Doig A, Marin Gutierrez FX, et al. Practical guidance for the development of Rosie, a health education question-and-answer Chatbot for new mothers. J Public Health Manag Pract 2023; 29:663–670. Han R, Todd A, Wardak S, et al. Feasibility and acceptability of Chatbots for nutrition and physical activity health promotion among adolescents: systematic scoping review with adolescent consultation. JMIR Hum Factors 2023; 10:e43227. Braga BC, Nguyen PH, Aberman NL, et al. Exploring an artificial intelligence-based, gamified phone app prototype to track and improve food choices of adolescent girls in Vietnam: acceptability, usability, and likeability study. JMIR Form Res 2022; 6:e35197. Pervanidou P, Chatzidaki E, Nicolaides NC, et al. The impact of the ENDORSE Digital Weight Management Program on the metabolic profile of children and adolescents with overweight and obesity and on food parenting practices. Nutrients 2023; 15:1777. Lee C, Kim S, Kim J, et al. Challenges of diet planning for children using artificial intelligence. Nutr Res Pract 2022; 16:801–812. Carrilho TRB, Silva NJ, Paixão ES, et al. Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol. BMJ Open 2023; 13:e073479. Banjar HR. A smart monitoring system for self-nutrition management in pediatric patients with inherited metabolic disorders: Maple Syrup Urine Disease (MSUD). Healthcare (Basel) 2023; 11:178. Alrige M, Banjar H, Shuaib T, et al. Knowledge-based dietary intake recommendations of nutrients for pediatric patients with Maple Syrup Urine Disease. Healthcare (Basel) 2023; 11:301. Yalçin N, Kaşikci M, Çelik HT, et al. Development and validation of machine learning-based clinical decision support tool for identifying malnutrition in NICU patients. Sci Rep 2023; 13:5227. Thanhaeuser M, Steyrl D, Fuiko R, et al. A secondary outcome analysis of a randomized trial using a mixed lipid emulsion containing fish oil in infants with extremely low birth weight: cognitive and behavioral outcome at preschool age. J Pediatr 2023; 254:68–74. e3. |
تواريخ الأحداث: | Date Created: 20240131 Date Completed: 20240405 Latest Revision: 20240531 |
رمز التحديث: | 20240601 |
DOI: | 10.1097/MCO.0000000000001018 |
PMID: | 38294876 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1473-6519 |
---|---|
DOI: | 10.1097/MCO.0000000000001018 |