Biomarker Intervention Classification for Personalized Chronic Care
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Solution Overview
Problem
Chronic disorders have reached epidemic proportions, posing significant risks for serious co-morbidities such as diabetes, cardiovascular disease, neurodegenerative diseases, and cancers, and existing technologies lack effective methods for determining personalized interventions using biomarkers.
Innovation Solution
A system and method utilizing a computing device to receive physiological data, detect biomarkers linked to maladies, classify them into intervention categories, generate combinations of these categories, and assign efficacy values to provide personalized interventions based on the assigned values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a computing device-based system is implemented to detect and classify biomarkers into intervention categories, then the precision of identifying suitable interventions is improved, but the device complexity increases
Solution Approach 1:
The system segments the complex task of intervention identification into distinct classification categories based on physiological data. Biomarkers are classified into multiple intervention categories (e.g., lifestyle modifications, pharmacological interventions, surgical interventions), allowing the system to manage complexity through structured segmentation while maintaining high precision in identifying suitable interventions for chronic disorders.
2Adaptability or versatility
If multiple intervention categories are generated and combined with efficacy values, then the adaptability of treatment plans is improved, but the computational requirements and system complexity increase
Solution Approach 1:
The system employs parameter changes by assigning efficacy values to different intervention categories based on physiological data and patient-specific factors. These efficacy parameters allow the system to dynamically adjust and optimize treatment plans, combining multiple intervention categories with varying efficacy weights to create adaptable, personalized treatment strategies for chronic disorders.
3Reliability
If comprehensive physiological data is analyzed to detect biomarkers and generate intervention combinations, then the reliability of predictive interventions is improved, but the loss of time for data processing increases
Solution Approach 1:
The system applies preliminary action by pre-establishing classification frameworks and intervention category structures before actual patient data analysis. The computing device is pre-configured with knowledge bases of biomarkers, intervention categories, and efficacy relationships, enabling rapid classification and intervention generation when physiological data is received, thus reducing processing time while maintaining high reliability.
Data Source
AI summary
A system for determining a predictive intervention using biomarkers, the system including a computing device configured to receive physiological data from the subject, detect, using the physiological data, at least a biomarker linked to a malady, classify at least the biomarker to a plurality of intervention categories for the malady, wherein each intervention category of the plurality of intervention categories is a distinct classification of intervention based on physiological data, generate at least a combination of the plurality of intervention categories, assign efficacy values to the combination of the plurality of intervention categories as a function of addressing the malady, and provide, to the user, the combination as a function of the assigned values.