Large Language Model-Based Text Recognition and Structured Data Extraction for Dietary Surveys

LLM-based text recognition and structured data extraction can serve as effective auxiliary tools to improve efficiency and accuracy in traditional dietary surveys. With the rapid advancement of artificial intelligence, more accurate and efficient auxiliary tools can be developed for more precise and efficient data collection in nutrition research.

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Publication Details

Journal
China CDC Weekly
Published
2026-01-01
DOI
https://doi.org/10.46234/ccdcw2026.008
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
0.00
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article

Large Language Model-Based Text Recognition and Structured Data Extraction for Dietary Surveys

Ouyang Yifei, Jia XiaoFang, ZHANG Jiguo, Niu RuiXue et al.
China CDC Weekly
Nutritional Studies and Diet
article

Large Language Model-Based Text Recognition and Structured Data Extraction for Dietary Surveys

Ouyang Yifei, Jia XiaoFang, ZHANG Jiguo, Niu RuiXue, DU Wenwen, Liu Honglei, Huang Feifei, Zhang Xiaofan, Wang Huijun, Guan Fangxu, Bai Jing, Su Chang, Wei Yanli, Li Li
article en

Abstract

LLM-based text recognition and structured data extraction can serve as effective auxiliary tools to improve efficiency and accuracy in traditional dietary surveys. With the rapid advancement of artificial intelligence, more accurate and efficient auxiliary tools can be developed for more precise and efficient data collection in nutrition research.

China CDC WeeklyVol. 8(2)
Chinese Center For Disease Control and Prevention (CN), National Health and Family Planning Commission (CN)
Zero hunger
Openalex Percentile: Top 93%
Nutritional Studies and Diet
DOIOpen Access PDFOpenAlex
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