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Topical Area: Precision Nutrition/Nutrient-Gene Interactions

Precision Nutrition/Nutrient-Gene Interactions (Poster Session)

(P10-015-23) Precision Nutrition With AI: An NLP-Based Model for Food Intake Analysis and Dietary Recommendations

Saturday, July 22, 2023
12:00 PM – 1:00 PM ET
Location: Poster Board 522

    Presenting Author(s)

  • Chien-Yeh Hsu, Ph.D

    Professor
    National Taipei University of Nursing and Health Sciences
    Taipei, Taipei, Taiwan (Republic of China)

    • Co-Author(s)

  • HL

    Hsiu An Lee, Ph.D.

    National Health Research Institutes
    New Taipei, New Taipei, Taiwan (Republic of China)

  • CL

    Chen-Yi Liu

    National Taipei University of Nursing and Health Sciences

  • PW

    Pin-Hua Wu

    National Taipei University of Nursing and Health Sciences

  • LY

    Lo-Hsien Yen

    National Taipei University of Nursing and Health Sciences

  • TH

    Tzu-Ting Huang

    National Taipei University of Nursing and Health Sciences

Disclosure(s):
Chien-Yeh Hsu, Ph.D: No relevant financial relationship(s) with ineligible companies to disclose.


Objectives: This approach emphasizes the process to tailor dietary recommendations to individual needs and preferences based on genetic, environmental, and lifestyle factors.

Methods: This research aims to develop an AI model for nutrition intake analysis using Natural Language Processing (NLP) techniques. The model is designed to extract food intake data from the food taking list or note provided by individuals, and then separate the food list into specific food ingredients. The nutrition information of these food ingredients is then calculated to determine the individual's overall nutrition intake.

Results: The use of NLP techniques in this model allows for accurate and efficient analysis of food intake data. By separating food intake into specific ingredients, the model can provide precise recommendations for individual dietary needs. This model has the potential to provide a personalized approach to nutrition that can help individuals achieve their health and wellness goals.

Conclusions: The proposed AI model for nutrition intake analysis represents a significant advancement in the field of precision nutrition. Its ability to analyze food intake data and provide tailored dietary recommendations has the potential to improve health outcomes for individuals. Further research is needed to validate the effectiveness of this model in real-world settings.

Funding Sources: National Science and Technology Council, Project No. 110-2221-E-227 -003 -MY3