Neural Network Treatment Mapping for Behavioral Adherence Tracking
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Solution Overview
Problem
Mapping the complex associations between health conditions, neurohumoral factors, and behaviors for effective treatment programs is challenging, and existing methods lack tools for accurately linking treatment programs to specific health conditions and tracking patient adherence.
Innovation Solution
A digital behavior-based treatment system that uses a neural network to correlate health conditions with neurohumoral factors and behaviors, providing personalized digital behavior and cognitive instructions, and tracks patient adherence through sensor data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is employed to determine correlations between health conditions, neurohumoral factors, and behaviors, then the accuracy of linking treatment programs to health conditions is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a neural network as an intermediary computational system that processes complex relationships between health conditions, neurohumoral factors, and behaviors. This intermediary layer enables accurate correlation mapping without requiring direct complex interactions between all system components, thus improving measurement precision while managing complexity through modular architecture.
Solution Approach 2:
The neural network system is designed to perform multiple functions: analyzing correlations between health conditions and neurohumoral factors, determining behavioral correlations, and generating treatment program recommendations. This multi-functionality consolidates what would otherwise require separate analytical systems into a single universal platform, improving accuracy without proportionally increasing complexity.
2Reliability
If real-time monitoring of patient adherence is implemented through sensor data analysis, then the reliability of treatment validation is improved, but the use of energy increases
Solution Approach 1:
The system implements continuous real-time monitoring of patient adherence through sensor data collection and analysis. This continuous operation ensures reliable treatment validation by constantly tracking behavioral compliance without interruption, maintaining high reliability throughout the treatment period rather than through periodic checks.
Solution Approach 2:
The patent replaces manual adherence assessment with automated sensor-based monitoring and neural network analysis. This substitution of mechanical/manual processes with electronic sensing and computational analysis improves reliability while the system optimizes energy consumption through efficient sensor management and selective data processing.
3Adaptability or versatility
If comprehensive behavioral and cognitive prescriptions are provided through digital instructions, then the adaptability of treatment programs is improved, but the difficulty of detecting and measuring adherence increases
Solution Approach 1:
The system implements comprehensive feedback mechanisms that continuously monitor patient adherence to behavioral and cognitive prescriptions through multiple sensors and digital tracking. This feedback loop provides real-time data on whether patients are following prescribed activities, enabling the neural network to adjust and optimize treatment programs while accurately measuring adherence to complex behavioral requirements.
Solution Approach 2:
The patent uses digital copies and representations of patient behaviors through sensor data, app interactions, and electronic records. These digital copies serve as measurable proxies for complex behavioral adherence, transforming difficult-to-measure physical and cognitive activities into quantifiable digital signals that can be analyzed by the neural network.
Data Source
AI summary
The present disclosure provides a method for generating treatment regimen for one or more health conditions, the method including retrieving a stored healthcare treatment model that has been trained to identify, for each of a plurality of health conditions, one or more respective treatment programs.