Translational Algorithms for Technological Dietary Quality Assessment Integrating Nutrimetabolic Data with Machine Learning Methods
This study highlights the potential of biochemical markers in predicting nutritional patterns and the development of algorithms for classifying dietary clusters, advancing dietary intake assessment technologies.
Authors
- Alfonso L. Calle‐Pascual (ORCID: https://orcid.org/0000-0002-3628-9323)
- Cristina M. Fernández-Díaz (ORCID: https://orcid.org/0000-0003-0539-7636)
- Juana María Sancho Gil (ORCID: https://orcid.org/0000-0002-2941-5619)
- Angélica Larrad-Sáinz (ORCID: https://orcid.org/0000-0001-7331-7672)
- Edwin Fernández‐Cruz (ORCID: https://orcid.org/0000-0001-9467-2788)
- Víctor de la O (ORCID: https://orcid.org/0000-0002-9916-275X)
- Miguel A. Rubio (ORCID: https://orcid.org/0000-0002-0495-6240)
- Ana Barabash (ORCID: https://orcid.org/0000-0003-2383-1563)
- Pilar Matía‐Martín (ORCID: https://orcid.org/0000-0001-9844-3755)
- J. Alfredo Martínez
Institutions
- Madrid Institute for Advanced Studies (ES)
- Universidad de Valladolid (ES)
- Universidad Complutense de Madrid (ES)
- Consejo Superior de Investigaciones Científicas (ES)
- Universidad de La Rioja (ES)
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (ES)
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (ES)
- Universidad Internacional De La Rioja (ES)
- IMDEA Food (ES)
- Universidad Autónoma de Madrid (ES)
Publication Details
- Journal
- Nutrients
- Published
- 2024-11-07
- DOI
- https://doi.org/10.3390/nu16223817
- Citations
- 3
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 1.63
Funders
- Comunidad de Madrid