AI Therapeutic Meal Planning With Genetic and Nutrient Modeling
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Solution Overview
Problem
Existing therapeutic meal plans are not personalized to individual patient needs, often based on outdated or inconsistent research, and lack integration of genetic profiles and complex nutrient interactions, making them ineffective for diverse patient populations.
Innovation Solution
An AI-based system using predictive analytics and machine learning models generates personalized therapeutic meal plans by analyzing patient profile data, incorporating genetic information, and continuously optimizing nutrient recommendations through neural networks and blockchain technology for secure data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional therapeutic meal plans are created based on general nutritional guidelines, then they are easy to implement and follow, but they fail to address individual patient needs and specific medical conditions
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing nutritional information, gene-nutrient interaction data, and meal composition databases before patient consultation. This allows the AI model to rapidly generate personalized meal plans during the consultation without requiring complex real-time calculations, thus maintaining ease of operation while achieving high personalization.
Solution Approach 2:
The patent introduces an AI-based predictive model as an intermediary between the practitioner and patient. This intermediary processes patient profile data, genetic information, and nutritional databases to generate evidence-based meal recommendations, eliminating the need for practitioners to manually analyze complex interactions while providing highly personalized plans.
2Reliability
If comprehensive patient profile data including genetic information is analyzed to create personalized meal plans, then treatment efficacy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex analysis task into distinct modules: patient profile data collection, genetic information processing, nutritional database querying, AI-based predictive modeling, and meal plan generation. Each module handles specific data types and operations independently, reducing overall system complexity while maintaining comprehensive analysis capabilities for improved treatment efficacy.
Solution Approach 2:
The patent transforms complex qualitative relationships between genes, nutrients, and diseases into quantifiable parameters that the AI model can process. By converting genetic profiles, nutrient interactions, and disease outcomes into structured numerical data, the system manages complexity while preserving the reliability needed for effective personalized treatment.
3Reliability
If evidence-based medicine approaches are used to ensure treatment reliability, then patient outcomes improve, but the time and resources required for research and validation increase
Solution Approach 1:
The system performs preliminary evidence synthesis by pre-processing and storing results from numerous clinical trials, systematic reviews, and meta-analyses in structured databases. When a patient consultation occurs, the AI model rapidly queries these pre-validated databases rather than conducting new research, delivering evidence-based recommendations in minutes rather than months.
Solution Approach 2:
The patent creates simplified copies of complex research findings by transforming detailed clinical trial data into standardized evidence profiles that capture essential relationships between nutrients, genes, and diseases. These copied evidence profiles can be rapidly retrieved and applied to multiple patient cases without requiring practitioners to review original research studies each time.
4Productivity
If real-time generation of personalized meal plans is implemented, then patient satisfaction and adherence improve, but computational resources and processing speed requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing optimal meal plan templates, nutrient combination recommendations, and genetic profile categorizations. During real-time patient consultations, the AI model selects and customizes from these pre-computed templates rather than generating plans from scratch, achieving rapid delivery while minimizing computational resource consumption.
Solution Approach 2:
The patent applies local quality by providing different levels of personalization based on patient needs and computational constraints. For routine cases, the system uses standardized templates with minimal customization requiring low computational resources. For complex cases involving multiple genetic variants or rare conditions, the system activates more intensive AI modeling only for those specific aspects, optimizing resource usage while maintaining real-time capability.
Data Source
AI summary
A system for real-time generation of therapeutic meal plans based on predictive analytics of patient profile data including a processor of a therapeutic plan server (TPS) node configured to host a machine learning (ML) module and connected to at least one patient-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive patient profile data from the at least one patient-entity node; parse the patient profile data to derive a plurality of key classifying features; query a local database to retrieve local historical patients-related data based on the plurality of key classifying features; generate at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of meal recommendation parameters from a meal plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector; and generate a meal plan for the at least one patient-entity node based on the plurality of meal recommendation parameters.