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Data Science and Machine Learning for Nutrition Studies

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition and Public Health".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 458

Special Issue Editors


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Guest Editor
1. Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
2. Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
Interests: medical image analysis; artificial intelligence; medical informatics

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Guest Editor
1. Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
2. Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
Interests: genomics; newborn medicine; brain; heart; nutrition

Special Issue Information

Dear Colleagues,

Big data, data science, and artificial intelligence (AI) have the potential to reshape nutrition research and the nutrition industry. Ongoing efforts have focused on the use of AI in dietary assessment, lifestyle intervention, nutrition–body interactions, nurture–nature associations, obesity management, dietary evaluation, personalized dietary suggestions, and the screening of malnutrition, to just name a few. Meanwhile, the increasing availability of diverse data, big-data, and multi-modal data—such as in birth cohorts, nutrition registries, retrospective electronic health records, imaging and genetic datasets, digitial-device-collected real-time biomedical data, and so on—is further enabling our understanding of nutrition’s role in the context of genetics, imaging, lifestyle, environement, and socioeconomic status. The field has seen a rapid movement toward personalized precision nutrition recommendations that are more comprehensive, (near) real-time, both in and outside clinical settings, and for not just vulnerable but also general populations. With this, the current Special Issue invites academic researchers and industry experts to share their latest discoveries in a wide range of topics, including but not limited to the following:

  • Big data nutrition studies;
  • AI in precision nutrition;
  • Chatbot for nutrition;
  • Food safety and quality control;
  • Public health nutrition and nutritional epidemiology;
  • Omics nutrition data integration;
  • Smartphone or mobile digital device-based nutrition studies;
  • Malnutrition prediction;
  • Ethical and regulation issues and policy making.

Dr. Yangming Ou
Dr. Sarah U. Morton
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • artificial intelligence
  • precision nutrition
  • personalized nutrition
  • multi-omics
  • cohort studies
  • nutritional epidemiology

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Published Papers (1 paper)

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Research

12 pages, 1759 KiB  
Article
Assessing Plant-Based Diets in Taiwan Using a Harmonized Food Description-Incorporated Framework
by Yu-Syuan Wei, Ming-Hua Lin, Fu-Jun Chen and She-Yu Chiu
Nutrients 2025, 17(14), 2268; https://doi.org/10.3390/nu17142268 - 9 Jul 2025
Viewed by 316
Abstract
Background: Exploring emerging dietary patterns, such as plant-based diets (PBD), often requires considerable effort to rebuild new systems or adapt existing food classification frameworks, presenting a substantial challenge for dietary research. Current systems were not originally designed for this purpose and vary [...] Read more.
Background: Exploring emerging dietary patterns, such as plant-based diets (PBD), often requires considerable effort to rebuild new systems or adapt existing food classification frameworks, presenting a substantial challenge for dietary research. Current systems were not originally designed for this purpose and vary in standardization and interoperability, complicating cross-study comparisons. This study aimed to adopt the harmonized, food description-incorporated, food classification system (HFDFC system) to develop a plant-based diet food classification system (PBDFC system), and to evaluate dietary intake and nutritional status among adults in Taiwan. Methods: A repeated cross-sectional design was applied using 24 h dietary recall data from the Nutrition and Health Survey in Taiwan (2013–2016 and 2017–2020), accessed via the national food consumption database. Adults aged 20–70 years were included. Data were processed through the HFDFC system to generate the PBDFC system. For each participant, the Plant-Based Diet Index (PDI), Body Mass Index (BMI), and Nutrient-Rich Food Index (NRF) were calculated and analyzed by age group. Results: Adults aged 46–70 had significantly higher O-PDI and H-PDI scores, lower Lh-PDI scores (all p < 0.0001), and higher NRF values. Despite higher average BMI, those in the highest H-PDI tertile had significantly lower BMI (p < 0.02). Conclusions: The HFDFC-based PBDFC system offers a flexible, scalable framework for plant-based diet classification and supports future cross-national research. Full article
(This article belongs to the Special Issue Data Science and Machine Learning for Nutrition Studies)
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