Relapse Prediction Model Using Relative Survival Regression
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Solution Overview
Problem
Current approaches fail to accurately predict and account for relapse in addiction treatment, particularly in identifying patients at high risk for relapse or non-adherence after initial treatment, and do not effectively provide timely interventions.
Innovation Solution
A predictive model using historical data from electronic health records, incorporating transformations, dimensionality reduction, and relative survival regression to identify risk factors and provide timely alerts for intervention, enabling personalized care plans and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current approaches are used to monitor addiction treatment patients, then resource consumption is low, but prediction accuracy of relapse risk is insufficient
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preprocessing patient data (demographics, treatment history, substance use patterns, biometric information) before relapse occurs. This proactive data accumulation and analysis enables early prediction of relapse risk, improving measurement precision without requiring complex real-time intervention systems.
Solution Approach 2:
The patent introduces an intermediary predictive analytics system that mediates between raw patient data and clinical decision-making. This intermediary layer processes electronic health records, biometric data, and treatment information through machine learning models to generate relapse risk predictions, simplifying the complexity for end-users while maintaining high prediction accuracy.
2Reliability
If timely interventions are implemented to prevent relapse, then treatment effectiveness improves, but time and resource consumption increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preprocessing patient data (demographics, treatment history, substance use patterns, biometric information) before relapse occurs. This proactive data accumulation and analysis enables early prediction of relapse risk, improving measurement precision without requiring complex real-time intervention systems.
Solution Approach 2:
The patent implements feedback mechanisms where relapse risk predictions are continuously communicated to healthcare providers and patients. This feedback loop enables timely adjustments to treatment plans based on predicted risk levels, improving treatment effectiveness while optimizing resource allocation by focusing interventions on high-risk periods and patients.
3Reliability
If comprehensive patient data is collected for accurate prediction, then prediction reliability improves, but data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary predictive analytics system that mediates between raw patient data and clinical decision-making. This intermediary layer processes electronic health records, biometric data, and treatment information through machine learning models to generate relapse risk predictions, simplifying the complexity for end-users while maintaining high prediction accuracy.
Solution Approach 2:
The system extracts and separates critical predictive features from comprehensive patient data using dimensionality reduction techniques and feature selection algorithms. By extracting only the most relevant predictors (such as treatment adherence patterns, biometric changes, and substance use indicators), the system maintains prediction reliability while reducing data processing complexity.
Data Source
AI summary
Technologies are provided for determining an individual's likelihood of relapsing into prior behavior subsequent to a treatment for a mental health or addiction disorder, and in some instances predicting a likelihood time frame for such a relapse. Target subjects having a risk of addiction relapse, non-adherence to a treatment program, or absconding, may be automatically identified based on a multiplicative-regression model for relative survival (MRS) that is developed for predicting risk or likelihood of relapse or non-adherence. Further, in some embodiments, a leading indicator of near-term future abnormalities may be provided thereby proactively notifying supervisory personnel responsible for the person and providing such personnel with timely notice to enable effective corrective, preventive, or trend-modifying maneuvers to be undertaken.