Personalized Diet System for IBD Trigger Food Identification
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Solution Overview
Problem
Current management approaches for inflammatory bowel diseases (IBD) and other immune-mediated inflammatory disorders (IMIDs) lack personalized and effective methods for identifying and eliminating trigger foods, leading to suboptimal symptom management and high healthcare costs.
Innovation Solution
A personalized care management system utilizing a digital platform with machine learning algorithms to identify and confirm trigger foods, guiding patients through phases of identification, elimination, reintroduction, and maintenance to develop a customized diet that ameliorates symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional management approaches are used for IBD and IMIDs, then treatment can be provided using conventional methods, but symptom management remains suboptimal and healthcare costs remain high
Solution Approach 1:
The system segments the diet identification process into distinct phases (identification, elimination, reintroduction, maintenance) and uses machine learning to analyze specific data types separately (food intake, symptoms, biomarkers) before integrating them for personalized recommendations, improving both effectiveness and efficiency
Solution Approach 2:
The system enables patients to actively participate in their own treatment by tracking their own food intake and symptoms, receiving personalized feedback, and making informed dietary decisions, reducing the need for continuous medical intervention while improving symptom management
2Reliability
If personalized dietary interventions are implemented to identify trigger foods, then symptom management improves, but the process becomes more complex and requires extensive data collection and analysis
Solution Approach 1:
The system uses a multi-functional machine learning platform that handles diverse data types (food intake, symptoms, biomarkers, genetic information) and performs multiple functions (pattern recognition, trigger identification, diet recommendation, progress tracking) within a single integrated system, managing complexity through consolidation
Solution Approach 2:
The machine learning algorithm acts as an intermediary that processes and interprets complex patient data, translating raw information into actionable dietary recommendations, thereby simplifying the interaction between patients and the complexity of personalized nutrition science
3Ease of operation
If conventional treatment methods are used, then treatment can be administered without personalized dietary assessment, but trigger foods remain unidentified and symptoms persist
Solution Approach 1:
The system performs preliminary dietary assessment and trigger food identification using machine learning analysis of patient data before implementing treatment, enabling personalized dietary recommendations that improve symptom relief effectiveness while maintaining ease of subsequent treatment administration
Data Source
AI summary
The invention is generally related to Inflammatory Bowel Disease, Irritable Bowel Syndrome and other immune-mediated inflammatory disorders (IMIDs) such as rheumatoid arthritis, the spondyloarthritis disease spectrum, connective tissue disorders, cutaneous inflammatory conditions, asthma and autoimmune neurological diseases such as multiple sclerosis, and more particularly to compositions and methods for management and amelioration of symptoms in a subject in need thereof. One embodiment of the present invention contemplates a system and method useful for managing and ameliorating IBD symptoms resulting in the development of a personalized diet for the IBD participant which will reduce or eliminate most IBD symptoms.