Neural Network System for Glucose Prediction
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Solution Overview
Problem
Current glucose monitoring and prediction systems for diabetes and trauma patients suffer from significant prediction errors and limited prediction windows, failing to accurately forecast glycemic fluctuations and optimize insulin dosages, leading to increased morbidity and mortality.
Innovation Solution
A neural network system with time-lagged feed-forward architecture, incorporating memory structures and adaptive learning capabilities, utilizes continuous glucose monitoring data to predict glucose levels and insulin dosages, accounting for various physiological and lifestyle factors, and provides real-time alerts for dysglycemia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional glucose monitoring systems are used, then the system structure is simple, but the prediction accuracy is low and prediction window is limited
Solution Approach 1:
The patent replaces traditional mechanical/statistical prediction methods with a neural network system that uses biological-inspired parallel processing and adaptive learning to achieve superior prediction accuracy while managing system complexity through software-based intelligence
Solution Approach 2:
The patent creates a composite predictive system that integrates multiple data sources (glucose measurements, insulin dosages, nutritional intake, exercise data, circadian rhythms) with neural network architecture, combining heterogeneous information types to achieve comprehensive glycemic prediction
2Measurement precision
If more factors are included in the model, then the prediction accuracy improves, but the model complexity increases
Solution Approach 1:
The patent designs a universal neural network architecture that can process multiple types of inputs (physiological data, lifestyle factors, environmental conditions) through a unified framework, allowing the same system structure to handle diverse data types without proportional increases in complexity
Solution Approach 2:
The patent implements dynamic weighting and adaptive learning mechanisms that allow the model to automatically adjust the importance of different factors based on current physiological states and historical patterns, enabling the system to handle complex multifactorial inputs while maintaining manageable computational requirements
3Productivity
If real-time prediction is implemented, then the clinical utility improves, but the computational requirements increase
Solution Approach 1:
The patent performs preliminary training and pattern recognition during offline periods using historical data, pre-computing neural network weights and parameters that can then be applied in real-time with minimal computational overhead, enabling rapid predictions without excessive energy consumption during critical monitoring periods
Solution Approach 2:
The patent implements continuous learning and adaptation mechanisms that update the neural network model progressively using incoming data streams, maintaining predictive accuracy over time while distributing computational load continuously rather than requiring intensive batch processing
Data Source
AI summary
A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time.