Machine Learning and Big Data in Nutritional Epidemiology
A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutritional Epidemiology".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 15
Special Issue Editors
Interests: medical statistics; cardiovascular and cancer epidemiology; development and validation of prognostic models; statistical methods for clinical research; clinical trials
Interests: gene profiling; prognostic biomarker and predictive biomarkers; machine learning; artificial intelligence; gene signatures
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) are increasingly shaping the field of nutritional epidemiology by enabling the analysis of complex, high-dimensional data characterized by non-linear relationships and multicollinearity. The growing availability of large-scale dietary, clinical, behavioral, social, and environmental datasets has created new opportunities to investigate diet–health relationships using advanced computational approaches.
Modern AI/ML methods, including deep learning and probabilistic models, support the identification of dietary patterns, enable risk stratification, and facilitate the development of precision nutrition strategies across populations. In addition, network-based and data-driven approaches allow the investigation of social connections, peer influences, and contextual factors that shape dietary behaviors and health outcomes. These methods support the integration of heterogeneous data sources, improved exposure assessment, and more accurate modeling of diet-related health outcomes over time.
This Special Issue invites contributions that advance methodological development or present robust applications of AI and ML in nutritional epidemiology. We welcome studies addressing multivariable modeling, data integration, model interpretability, uncertainty assessment, as well as issues related to bias, transparency, and reproducibility. By promoting innovative and reliable analytical frameworks, this Special Issue aims to strengthen evidence-based nutritional research and support the development of more effective public health and nutrition strategies.
Prof. Dr. Paolo Chiodini
Dr. Mario Fordellone
Guest Editors
Manuscript Submission Information
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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
- nutritional epidemiology
- machine learning
- artificial intelligence
- big data
- precision nutrition
- dietary patterns
- data integration
- model interpretability
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