Year: 2026
Type: article
Abstract: An important goal for personalized diet systems is to improve nutritional quality without compromising convenience or affordability. We present an end-to-end framework that converts dietary standards into complete meals with few ingredient changes. Using the What We Eat in America (WWEIA) intake data for 135,491 meals, we identify 34 interpretable meal archetypes that we then use to condition a generative model and a portion predictor to meet USDA nutritional targets. In comparisons within archetypes, generated meals show a median 47.0% reduction in absolute deviation from per-meal RDI targets, while remaining compositionally close to real meals. Our results show that by allowing one to three food substitutions, we were able to create meals that were 10% more nutritious, while reducing costs 19-32%, on average. By turning dietary guidelines into realistic, budget-aware meals and simple swaps, this framework can support public-health programs and consumer apps; clinical decision support is a promising future direction pending further validation and safety review, to deliver scalable, equitable improvements in everyday nutrition.
Source: PLOS Digital Health
Authors: Trevor Chan, Ilias Tagkopoulos
Institution: University of California, Davis
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,043
OpenAlex ID: W7162697245