Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26988, first published .
Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Journals

  1. Xing W, Gao W, Zhao Z, Xu X, Bu H, Su H, Mao G, Chen J. Dietary flavonoids intake contributes to delay biological aging process: analysis from NHANES dataset. Journal of Translational Medicine 2023;21(1) View
  2. Liu Y, Yin T, He M, Fang C, Peng S. The relationship of dietary flavonoids and periodontitis in US population: a cross-sectional NHANES analysis. Clinical Oral Investigations 2024;28(3) View
  3. Sosa-Holwerda A, Park O, Albracht-Schulte K, Niraula S, Thompson L, Oldewage-Theron W. The Role of Artificial Intelligence in Nutrition Research: A Scoping Review. Nutrients 2024;16(13):2066 View
  4. Wang S, Xiong F, Liu Y, Feng Z. Exploring flavonoid intake and all-cause mortality in diverse health conditions: Insights from NHANES 2007–2010 and 2017–2018. Nutrition 2024;127:112556 View
  5. Zheng J, Wang J, Shen J, An R. Artificial Intelligence Applications to Measure Food and Nutrient Intakes: A Scoping Review (Preprint). Journal of Medical Internet Research 2023 View

Books/Policy Documents

  1. Côté M, Lamarche B. Artificial Intelligence in Clinical Practice. View