Exploring approaches to computational representation and classification of user-generated meal logs

• URL: http://arxiv.org/abs/2509.06330v1
• Date: Mon, 08 Sep 2025 04:23:48 GMT
• Title: Exploring approaches to computational representation and classification of user-generated meal logs
• Authors: Guanlan Hu, Adit Anand, Pooja M. Desai, Iñigo Urteaga, Lena Mamykina,
• Abstract summary: This study examined the use of machine learning and domain specific enrichment on patient generated health data to classify meals on alignment with different nutritional goals.<n>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.
• Score: 6.888077368936294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study examined the use of machine learning and domain specific enrichment on patient generated health data, in the form of free text meal logs, to classify meals on alignment with different nutritional goals. 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 provided expert judgement for meal to goal alignment, used as gold standard for evaluation. Using text embeddings, including TFIDF and BERT, and domain specific enrichment information, including 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 self assessment. Even without enrichment, ML outperformed self assessments of individuals who logged meals, and the best performing combination of ML classifier with enrichment achieved even higher accuracies. 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. In conclusion, ML can utilize unstructured free text meal logs and reliably classify whether meals align with specific nutritional goals, exceeding 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.

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