Neural Network Relapse Prediction for Addiction Treatment
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Solution Overview
Problem
Current methods for predicting relapse in addiction treatment rely on manual reporting, which is often inaccurate, untimely, or incomplete, necessitating a more efficient and automated approach to provide timely support to healthcare professionals and individuals in recovery.
Innovation Solution
A neural network algorithm that leverages personal data, including physiological, psychological, and environmental factors, using Generative Pre-trained Transformer (GPT) models for real-time intervention, integrated into wearable devices, mobile applications, and telemedicine systems to predict addiction relapse risk and generate personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reporting methods are used for relapse prediction, then implementation simplicity is maintained, but accuracy and timeliness of relapse detection deteriorate
Solution Approach 1:
The patent replaces manual reporting mechanisms with an automated neural network system that processes physiological data from wearable devices. This substitution eliminates human error in data collection and provides continuous, objective monitoring of relapse risk indicators, thereby improving detection accuracy while accepting increased system complexity.
Solution Approach 2:
The system enables self-monitoring through wearable devices that automatically collect physiological data without requiring active patient participation or manual reporting. The neural network autonomously analyzes this data to predict relapse risk, allowing the system to serve itself in data collection and analysis, improving both accuracy and timeliness.
2Loss of time
If manual data collection is used, then system simplicity is maintained, but timeliness of intervention deteriorates
Solution Approach 1:
The patent implements continuous monitoring through wearable devices that constantly collect physiological data, eliminating the discontinuous nature of manual reporting. The neural network continuously processes this data stream to detect relapse risk in real-time, ensuring timely intervention while requiring a high level of automation to maintain continuous operation.
Solution Approach 2:
The system performs preliminary analysis of physiological data continuously to detect early signs of relapse risk before actual relapse occurs. The neural network identifies patterns and trends in advance, enabling proactive intervention rather than reactive response, thereby reducing intervention response time while necessitating automated real-time processing.
3Measurement precision
If comprehensive personal data is collected, then prediction accuracy improves, but data privacy risks increase
Solution Approach 1:
The patent introduces the neural network as an intermediary that processes sensitive physiological data without requiring direct human review. The system aggregates and analyzes data from multiple sources (wearable devices, electronic health records) through the neural network, which transforms raw personal data into anonymized risk predictions, thereby improving prediction accuracy while mitigating privacy risks through automated processing and data minimization.
Data Source
AI summary
Methods and systems for predicting addiction relapse risk in a patient include; receiving patient data of a patient; predicting, by a trained neural network model, based on the patient data, an addiction relapse risk in the patient; generating a recommendation based on the predicted addiction relapse risk; and communicating the recommendation to a relevant party.