This paper is in the following e-collection/theme issue:

Innovations and Technology for Healthy Eating Education (400) Natural Language Processing (644) Mobile Health (mhealth) (2765) Instruments and Questionnaires for Nutrition and Food Intake (159) mHealth for Data Collection and Research (914) mHealth for Wellness, Behavior Change and Prevention (2743) New Methods (909) Machine Learning (1384)

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

Authors of this article:

Salima Taylor1 Author Orcid Image ;   Mandy Korpusik2 Author Orcid Image ;   Sai Das1 Author Orcid Image ;   Cheryl Gilhooly1 Author Orcid Image ;   Ryan Simpson3 Author Orcid Image ;   James Glass2 Author Orcid Image ;   Susan Roberts1 Author Orcid Image
Article Authors Cited by (4) Tweetations (5) Metrics

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  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

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