A method for a computing system to provide a food and/or dietary recommendation and a predicted effects profile, the computing system including:
- at least one processor;
- a communication interface communicatively coupled to the at least one processor;
- and
- a memory device storing executable code that, the method comprising, when the executable code is executed, the at least one processor causing the computing system to:
- receive, from a user device, user-specific personal data comprising user-specific biological metrics;
- receive, from the user device, user selection inputs comprising user-specific dietary and food consumption data;
- receive, from the user device, user selection inputs comprising user-specific health outcome and prediction model preference data;
- automatically generate and store, in a system-internal database, a user-specific health and consumption profile based at least on the user-specific personal data and user selection inputs;
- access at least one external database and retrieve therefrom evidence-based data associated with at least portions of the user-specific health and consumption profile;
- generate a food recommendation data object based at least in part on the user-specific social, environmental, and biological metrics and retrieved evidence-based data;
- generate a food recommendation data object aligned with existing dietary guidelines or patterns based at least in part on retrieved evidence-based data containing multiple dietary quality metrics and/or indices and their listed scoring components;
- generate a food recommendation data object aligned with expert's food healthfulness opinions based at least in part on retrieved evidence-based data containing multiple food quality metrics and/or indices and their listed scoring components identified and pooled from the scientific literature on nutritional profiling systems/algorithms, including an assessment of the consensus food rating across multiple expert-driven algorithms;
- generate a dietary recommendation data object based at least in part on the user-specific biological metrics and retrieved evidence-based data;
- generate a dietary recommendation data object based on the retrieved evidence-based data containing clinical effect sizes for health outcomes stratified by dietary quality index/measure/guideline;
- generate a multi-dimensional, visual dietary recommendation data object known as a spider or radar chart based on the user-specific dietary quality and retrieved evidence-based data containing multiple dietary quality metrics and/or indices;
- generate, by predictive modeling, a predicted effect data object based at least in part on the dietary recommendation data object and the user-specific biological metrics that are not blood glucose;
- generate, by either predictive modeling and/or via a prior belief elicitation method, an uncertainty data object based on a survey question and/or framework coded into a prior distribution of effect size or relative variable importance, the degree of missingness in the food and or diet data, the variation/distribution in prediction results across multiple predictive models, the distribution of predictions resulting from the same model, variation in the target prediction task, and/or variation in individuals and subgroups;
- generate, by an expert-belief elicitation method using nutritional profiling algorithms that reflect various expert beliefs on food healthfulness, an uncertainty data object based the variation/distribution in prediction results across multiple predictive models, the distribution of predictions resulting from the same model, variation in the target prediction task, and/or variation in individuals and subgroups;
- generate, by either predictive modeling and/or via a prior belief elicitation method, a quality data object based on a survey question coded into a prior distribution of effect size or importance, a measure to quantify the degree of accuracy of the prediction, the variation in predicted results across multiple predictive models ranked by their predictive accuracy, and/or based on variation in accuracy among individuals and subgroups;
- and
- transmit for display, at least in part, on the user device:
- the food recommendation data object;
- the dietary recommendation data object
- the predicted effect data object;
- the uncertainty data object;
- and
- the quality data object.