Dynamic Metabolic Health Modeling via ML Biosignal Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional disease management platforms fail to address the root causes of metabolic health deterioration and do not provide personalized recommendations for improving metabolic health, as they ignore important markers such as blood sugar dysregulation and the effects of specific foods and activities on individual metabolic states.
Innovation Solution
A patient health management platform using machine-learning models to analyze biosignals and provide personalized recommendations for food and activity based on individual metabolic responses, incorporating data from wearable sensors, lab tests, and lifestyle data to classify food items and activities and generate tailored nutrition and exercise plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional disease management platforms are used, then general disease treatment is provided, but personalized metabolic health management and root cause treatment are not achieved
Solution Approach 1:
The system dynamically adapts to each patient's unique metabolic profile by continuously learning from their biosignal data, food intake records, and activity logs. Machine learning models are personalized for each patient and updated over time, allowing the system to evolve and provide increasingly accurate personalized recommendations rather than using static general guidelines
Solution Approach 2:
The system automatically collects and processes biosignal data from wearable sensors, lab tests, and patient-reported information without requiring manual intervention. The machine learning models autonomously analyze this data to generate personalized metabolic profiles and recommendations, reducing the need for complex manual assessments by healthcare providers
2Measurement precision
If machine learning models are implemented for personalized metabolic profiling, then accurate patient-specific insights are generated, but data processing complexity and computational requirements increase
Solution Approach 1:
The system segments the complex metabolic analysis into distinct machine learning models: one model predicts metabolic responses to food items while another predicts responses to physical activities. This segmentation allows each model to specialize in specific aspects of metabolic analysis, improving accuracy while making the overall system more manageable and interpretable
Solution Approach 2:
The system introduces a digital twin as an intermediary representation of the patient's virtual metabolic system. This digital twin serves as a computational model that simulates and predicts metabolic responses to various inputs, bridging the gap between raw data and clinical insights without requiring direct complex computational analysis of all patient data
3Reliability
If comprehensive biosignal monitoring is implemented, then metabolic state tracking is improved, but patient burden and data collection complexity increase
Solution Approach 1:
The system automatically collects biosignal data through wearable sensors that continuously monitor metabolic markers without requiring active patient participation. Lab tests and other measurements are integrated automatically, and the system processes all this data autonomously to generate insights, minimizing the effort required from patients while maintaining high monitoring accuracy
Solution Approach 2:
The system integrates multiple data collection methods into a unified platform that handles biosignal data from wearables, lab test results, food intake records, and activity logs through a single interface. This multi-functional approach consolidates various monitoring tasks into one system, reducing patient burden compared to using separate tools for each type of data collection
Data Source
AI summary
Disclosed herein is a method, system, and computer-readable medium for recommending foods to a patient. The disclosure includes accessing a record of food items recorded by a patient, including a classification of each food item. The method retrieves a current metabolic profile of the patient. Using a machine learning model, the method determines an updated classification for the food items and generates a notification for the patient. Additionally disclosed is a method, system, and computer-readable medium for recommending activities and activity times to a patient. The method includes accessing a record of activities previously recorded by a patient, each entry in the recordings including a duration of the activity and biosignal measurements. The method determines an effect of each activity on the metabolic state of the patient using a machine learning model. The method identifies activities that improve the patient's metabolic state and generates a recommendation for the patient.