Unhealthy diets are a leading cause of major chronic diseases including obesity, diabetes, cancer, and heart disease. Food environments--the physical spaces in which people access and consume food--have the potential to profoundly impact diet and related diseases. We take a step towards better understanding the nutritional quality of food environments by developing MINT: Menu Item to NutrienT model. This model utilizes under-studied data sources on recipes and generic food items, along with state-of-the-art word embedding and deep learning methods, to predict the nutrient density of never-before-seen food items using only their name as input. The model achieves an R^2=0.77, a substantial improvement over comparable models. We illustrate the utility of MINT by applying it to the Los Angeles restaurant food environment, and discover close agreement between predicted and ground truth nutrient density of restaurant menu items. This model represents a significant step towards a policy toolkit needed to precisely identify and target food environments characterized by poor nutritional quality.

The authors have declared no competing interest.

This work was funded by the National Institute of Minority Health and Health Disparities (NIMHD) of the National Institutes of Health under award number P50MD017344, to the Southern California Center for Latino Health (SCCLH).

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The data used in this study were accessed through public domain websites and APIs. The Edamam data on generic food items is available from the Edamam Food Database API, found at: https://www.edamam.com/. The Spoonacular data on chain restaurant menus is available from their API, found at https://spoonacular.com/food-api. The Recipe1M+ data can be accessed directly from their website at http://pic2recipe.csail.mit.edu/.

https://github.com/alexdseo/mint