Academic Journal
Exploring approaches to computational representation and classification of user-generated meal logs.
| Τίτλος: | Exploring approaches to computational representation and classification of user-generated meal logs. |
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| Συγγραφείς: | Hu G; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, United States.; The Data Science Institute, Columbia University, New York, NY, 10032, United States., Anand A; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, United States., Desai PM; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, United States., Urteaga I; BCAM-Basque Center for Applied Mathematics & IKERBASQUE-Basque Foundation for Science, Bilbao, Spain., Mamykina L; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, United States.; The Data Science Institute, Columbia University, New York, NY, 10032, United States. |
| Πηγή: | Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2026 Apr 01; Vol. 33 (4), pp. 836-846. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2015- : Oxford : Oxford University Press Original Publication: Philadelphia, PA : Hanley & Belfus, c1993- |
| Ιατρικοί όροι (MeSH): | Meals* , Machine Learning* , Patient Generated Health Data*, Humans ; Classification Algorithms ; Mobile Applications ; Multilayer Perceptrons ; Logistic Models ; Self-Assessment |
| Περίληψη: | Objective: This study examined the use of machine learning (ML) and domain-specific enrichment in patient-generated health data, in the form of free-text meal logs, to classify meals on alignment with different nutritional goals. Materials and Methods: We used a dataset of over 3000 meal records collected by 114 individuals from a diverse, low-income community in a major US city using a mobile app. Registered dietitians (RDs) provided expert judgment for meal-goal alignment, used as the "gold-standard" for evaluation. Using text embeddings (TF-IDF and BERT) and domain-specific enrichment information (ontologies, ingredient parsers, and macronutrient contents) as inputs, we evaluated the performance of logistic regression and multilayer perceptron classifiers using accuracy, precision, recall, and F1 score against the gold standard and the individual's self-assessment. Results: On average, individuals who logged meals achieved 0.576 accuracy of meal-goal alignment self-assessments. Even without enrichment, ML outperformed individual's self-assessments, with accuracies within 0.726-0.841 for different goals. The best-performing combination of ML classifier with enrichment achieved even higher accuracies (0.814-0.902). In general, ML classifiers with enrichment of parsed ingredients, food entities, and macronutrients information performed well across multiple nutritional goals, but there was variability in the impact of enrichment and classification algorithm on accuracy of classification for different nutritional goals. Conclusion: ML can utilize unstructured free-text meal logs and reliably classify whether meals align with specific nutritional goals, exceeding individuals' self-assessments, especially when incorporating nutrition domain knowledge. Our findings highlight the potential of ML analysis of patient-generated health data to support patient-centered nutrition guidance in precision healthcare. (© The Author(s) 2026. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.) |
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| Grant Information: | R01DK113189 United States DK NIDDK NIH HHS; T15 LM007079 United States LM NLM NIH HHS; T15LM007079 United States LM NLM NIH HHS; Basque Government; SCH : 2306690 National Science Foundation; 2306690 National Science Foundation; R01 DK113189 United States DK NIDDK NIH HHS |
| Contributed Indexing: | Keywords: embedding; machine learning; nutrition intake; patient-generated health data; personalized nutrition |
| Entry Date(s): | Date Created: 20260314 Date Completed: 20260710 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13089452 |
| DOI: | 10.1093/jamia/ocaf200 |
| PMID: | 41830956 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1527-974X |
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| DOI: | 10.1093/jamia/ocaf200 |