Risk Prediction Model for Disparity-Adjusted Treatment Prioritization
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Solution Overview
Problem
Existing computer-based risk prediction techniques for diseases such as cognitive impairment and heart disease are inadequate as they fail to reliably detect high-risk individuals and generate tailored treatment pathways, due to limitations in data types and regional inequalities.
Innovation Solution
A machine-learning-based risk prediction model that integrates various data types, including cognitive scores, electronic health records, and image scans, to generate individual risk scores, disparity-adjusted risk scores, and phenotypic profiles, enabling targeted treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional population-level risk prediction models are used, then resource consumption is reduced, but measurement precision and reliability of high-risk entity detection deteriorate
Solution Approach 1:
The patent segments the risk prediction process into multiple stages: initial population-level screening followed by targeted individual-level analysis only for high-risk candidates. This segmentation allows the system to maintain high measurement precision for risk detection while reducing overall computational resource consumption by applying intensive processing only where necessary.
Solution Approach 2:
The patent applies partial action by performing comprehensive multi-modal data analysis only on a subset of the population identified as high-risk through initial screening. Rather than analyzing all individuals with equal depth, the system concentrates computational resources on those most likely to benefit, achieving high prediction accuracy for critical cases while optimizing resource utilization.
2Measurement precision
If comprehensive multi-modal data integration is implemented, then measurement precision of risk predictions improves, but device complexity increases
Solution Approach 1:
The patent introduces intermediary components including a standardized data integration layer and phenotypic profile generation module that mediate between diverse data sources (cognitive scores, EMR data, image scans, remote monitoring data) and the risk prediction engine. These intermediaries harmonize different data formats and types, enabling comprehensive multi-modal integration while managing system complexity through modular architecture.
Solution Approach 2:
The patent transforms diverse data types from different modalities into unified phenotypic profiles with standardized parameters. By converting cognitive scores, electronic health records, image scans, and remote monitoring data into comparable parametric representations, the system achieves comprehensive data integration while maintaining manageable complexity through parameter standardization.
3Measurement precision
If staged evaluation and reintegration processes are implemented, then measurement precision and adaptability improve, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by conducting initial risk assessment using available data before triggering staged evaluations. High-risk individuals identified through preliminary screening undergo more intensive staged evaluations with reintegration of results, while low-risk individuals receive immediate results. This preliminary action approach refines prediction accuracy for those who need it while minimizing processing time for the majority.
Solution Approach 2:
The patent implements periodic action through staged evaluations that are triggered at specific intervals or conditions rather than continuously for all individuals. The system periodically re-evaluates high-risk candidates and reintegrates evaluation results back into the risk prediction model, achieving refined accuracy while controlling processing time through conditional, periodic rather than continuous operation.
Data Source
AI summary
Various embodiments of the present disclosure provide machine learning model-based risk prediction and treatment pathway prioritization for entities associated with a respective disparity group. Example embodiments are configured to generate, using a risk prediction model, an individual risk score for an entity of a disparity group associated with an entity cohort. Example embodiments are also configured to generate, using a disparity risk adjustment model, a disparity adjusted risk score for the entity based on the individual risk score. Example embodiments are also configured to initiate various prediction-based actions for the entity based on a comparison between the disparity adjusted risk score and a risk score threshold. Example embodiments are also configured to generate a phenotypic profile for the entity based on an evaluation data object and an image-based evaluation data object for the entity and generate a prediction-based action sequence for the entity based on the phenotypic profile.