Faculty, Staff and Student Publications
Language
English
Publication Date
2-1-2026
Journal
JAMIA Open
DOI
10.1093/jamiaopen/ooaf181
PMID
41531742
PMCID
PMC12794014
PubMedCentral® Posted Date
1-7-2026
PubMedCentral® Full Text Version
Post-print
Abstract
Objectives: Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) computable phenotypes to identify high-fat diet (HFD) using variables typically available in EHRs.
Materials and methods: We used National Health and Nutrition Examination Survey (NHANES) 1999-2020 data, where 24-h dietary recall served as ground truth. Dietary fat intake was summarized into a score (0-30) based on percent energy from fat, carbohydrate, and protein; lower scores indicated HFD. We defined HFD at cutoffs of 10, 15, and 20, and trained ML models (Extreme Gradient Boosting, logistic regression, random forest) using EHR-compatible variables (demographics, comorbidities, labs, anthropometrics). Model interpretability was assessed using Shapley Additive Explanations. To evaluate clinical relevance, we compared cancer associations using ML-predicted vs true diet labels.
Results: Machine learning models classified HFD with good performance, strongest at broader definitions. Random forest achieved an F1-score of 0.79 (recall 0.74, precision 0.84) at cutoff 20. Key predictors included race/ethnicity, triglycerides, obesity metrics (body mass index and derived indices), and metabolic panel results.
Discussion: These findings indicate that dietary patterns, though seldom recorded in EHRs, can be inferred from routinely available variables. The ability of ML-derived phenotypes to reproduce known diet-disease relationships underscore their epidemiologic validity. Top predictors also align with established biological pathways linking obesity, lipid metabolism, and cancer risk, supporting plausibility.
Conclusion: A high-fat dietary pattern can be inferred from EHR-compatible variables using ML-based phenotyping. This approach offers a scalable tool to integrate diet into EHR-based research and precision medicine.
Keywords
high-fat dietary, machine learning, computable phenotyping, electronic health records
Published Open-Access
yes
Recommended Citation
Yeh, Ya-Yun; Lin, Hsin-Yueh; Guo, Jingchuan; et al., "Inferring High-Fat Dietary Patterns From Electronic Health Record Data Using Machine Learning" (2026). Faculty, Staff and Student Publications. 890.
https://digitalcommons.library.tmc.edu/uthshis_docs/890