Automated Dietary Coding

Automated Dietary Coding is a computational method that systematically assigns standardized dietary codes to food and beverage intake descriptions using algorithmic or machine learning techniques, distinguishing it from manual coding by its reliance on automated data processing for dietary assessment.

5

Publications

414

Citations

33

Authors

12

Institutions

Publications per year

2017–2026

1

Authors

33

  1. DF
    Diane Feskanich

    Harvard University, United States

    1 publications · 60 citations

    1

    h-index

  2. MM
    Michelle Morris

    University of Leeds, United Kingdom

    1 publications · 173 citations

    1

    h-index

  3. NH
    Neil Hancock

    University of Leeds, United Kingdom

    1 publications · 173 citations

    1

    h-index

  4. NS
    Narpinder Singh

    Guru Nanak Dev University, India

    1 publications · 34 citations

    1

    h-index

  5. UZ
    Umme Z. Mulla

    Imperial College London, United Kingdom

    1 publications · 173 citations

    1

    h-index

1–5 of 33

Venues

  1. Journal of Human Nutrition and Dietetics

    1 publications · 127 citations

    1

    h-index

  2. Journal of the American Dietetic Association

    JADA · 1 publications · 60 citations

    1

    h-index

  3. Nutrients

    1 publications · 173 citations

    1

    h-index

  4. Systems Microbiology and Biomanufacturing

    1 publications · 34 citations

    1

    h-index

  5. Journal of Parenteral and Enteral Nutrition

    1 publications · 20 citations

    1

    h-index

1–5