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임치현

Lim, Chiehyeon
Service Engineering & Knowledge Discovery Lab.
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dc.citation.endPage 812 -
dc.citation.number 6 -
dc.citation.startPage 801 -
dc.citation.title NUTRITION RESEARCH AND PRACTICE -
dc.citation.volume 16 -
dc.contributor.author Lee, Changhun -
dc.contributor.author Kim, Soohyeok -
dc.contributor.author Kim, Jayun -
dc.contributor.author Lim, Chiehyeon -
dc.contributor.author Jung, Minyoung -
dc.date.accessioned 2023-12-21T13:13:29Z -
dc.date.available 2023-12-21T13:13:29Z -
dc.date.created 2022-12-26 -
dc.date.issued 2022-12 -
dc.description.abstract BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications.MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human-and AI-generated diets in 2 steps.RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human-and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information).CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria. -
dc.identifier.bibliographicCitation NUTRITION RESEARCH AND PRACTICE, v.16, no.6, pp.801 - 812 -
dc.identifier.doi 10.4162/nrp.2022.16.6.801 -
dc.identifier.issn 1976-1457 -
dc.identifier.scopusid 2-s2.0-85143229213 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/60464 -
dc.identifier.wosid 000890486900010 -
dc.language 영어 -
dc.publisher KOREAN NUTRITION SOC -
dc.title Challenges of diet planning for children using artificial intelligence -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Nutrition & Dietetics -
dc.identifier.kciid ART002902157 -
dc.relation.journalResearchArea Nutrition & Dietetics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.description.journalRegisteredClass kci -
dc.subject.keywordAuthor Children -
dc.subject.keywordAuthor dieticians -
dc.subject.keywordAuthor artificial intelligence -
dc.subject.keywordAuthor diet planning -
dc.subject.keywordPlus OBESITY PREVENTION -
dc.subject.keywordPlus NUTRITION -
dc.subject.keywordPlus CARE -
dc.subject.keywordPlus GUIDELINES -
dc.subject.keywordPlus POSITION -
dc.subject.keywordPlus IMPLEMENTATION -
dc.subject.keywordPlus SERVICE -

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