Interdependent Biomarker Time-Series Models for Disease Staging
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Solution Overview
Problem
Existing biomarker models are optimized for single disease endpoints, lacking predictive power for multiple outcomes and often require separate training for each stage, leading to inefficiencies and inaccuracies in disease progression prediction.
Innovation Solution
A method for learning interdependent biomarkers using integrated time-series machine learning models that predict disease stages and treatment responses by jointly training on multiple endpoints, employing techniques like recurrent neural networks and transformer-based architectures with causal attention, and utilizing genomics data from blood samples to forecast disease progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate biomarker models are trained for each disease endpoint, then each model can be optimized for its specific endpoint, but the overall system complexity increases and predictive power for multiple outcomes decreases
Solution Approach 1:
The patent merges multiple separate biomarker models into a single integrated model that simultaneously predicts multiple disease endpoints. This unified approach allows the system to capture interdependencies between different outcomes and shared biomarker patterns, reducing overall system complexity while maintaining or improving predictive power across all endpoints.
Solution Approach 2:
The integrated biomarker model serves multiple functions by predicting various disease endpoints simultaneously. A single model structure processes biomarker data to generate predictions for different outcomes, eliminating the need for separate specialized models and reducing computational and operational complexity.
2Measurement precision
If biomarkers are trained independently for each outcome, then training efficiency is improved, but accuracy in predicting disease progression and treatment response decreases
Solution Approach 1:
The patent combines multiple training tasks into a single integrated training process that simultaneously learns biomarker patterns for different outcomes. This approach leverages shared patterns across endpoints while capturing outcome-specific nuances, improving prediction accuracy without requiring separate training pipelines.
Solution Approach 2:
The integrated model maintains continuous learning across all outcomes simultaneously, allowing biomarker patterns to be learned in context of multiple endpoints rather than sequentially. This continuous multi-outcome training improves the model's ability to predict disease progression and treatment response accurately.
3Device complexity
If generalized linear models are used for biomarker prediction, then model simplicity is maintained, but the ability to capture interdependent biomarkers and time-series patterns is limited
Solution Approach 1:
The patent transitions from static generalized linear models to dynamic time-series models that capture temporal patterns and dependencies in biomarker data. This dynamic approach models how biomarkers evolve over time and interact with each other, significantly improving predictive accuracy for disease progression and treatment response.
Solution Approach 2:
The patent adds temporal dimensionality to biomarker prediction by modeling time-series patterns. This dimension allows the model to capture dynamics such as biomarker trajectories, rates of change, and temporal relationships between markers, which are critical for predicting disease progression but cannot be captured by static models.
Data Source
AI summary
Methods and systems for patient stratification include learning interdependent biomarkers as integrated time-series machine learning models. A disease stage is identified for a patient based on collected biomarker data. A treatment for the patient is performed based on the identified disease stage and a predicted future response of the patient.