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

Abstract

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. 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. 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. 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.