Data-Driven Multi-Scale Model Reduction for Heart Function
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Solution Overview
Problem
Current multi-scale models for heart function are computationally demanding due to numerous parameters, making them challenging to personalize for specific genetic groups or individual patients, and existing model reduction techniques have not effectively addressed this complexity.
Innovation Solution
A method using dimensionality reduction techniques and data-driven generative models to identify and reduce the number of clinically observable parameters, allowing for patient-specific multi-scale computations by generating a database of physiological measurements and learning a data-driven model that captures the output of the original multi-scale model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed sub-cellular models with numerous parameters are used to accurately describe molecular pathways, then model accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and identifies the most influential parameters from the detailed sub-cellular models using sensitivity analysis and dimensionality reduction techniques. By separating the critical parameters from the less important ones, the model maintains accuracy for the most impactful variables while reducing overall computational complexity.
Solution Approach 2:
The patent transforms the original high-dimensional parameter space into a reduced parameter space through dimensionality reduction techniques. This allows the model to operate with fewer parameters while preserving the essential dynamics and accuracy of the original detailed models.
2Reliability
If numerous parameters are used to capture multi-scale dynamics, then model fidelity is improved, but model personalization becomes difficult
Solution Approach 1:
The patent extracts the key parameters that drive multi-scale dynamics and focuses personalization efforts on these reduced set of parameters. This makes model personalization feasible by reducing the burden of estimating numerous parameters while maintaining fidelity through the retained critical variables.
Solution Approach 2:
The patent performs preliminary dimensionality reduction and parameter identification to prepare the model for personalization. By pre-identifying the influential parameters and reducing the parameter space beforehand, the model becomes more amenable to personalization for specific genetic groups, populations, or individual patients.
3Measurement precision
If detailed multi-scale models are used to describe organ physiology, then physiological accuracy is improved, but computational efficiency decreases
Solution Approach 1:
The patent creates a reduced-order copy of the detailed multi-scale model that replicates the essential physiological behavior. This simplified model copy maintains physiological accuracy for key outputs while achieving significant computational efficiency improvements, enabling faster simulations and broader applications.
Data Source
AI summary
A method of computing physiological measurements resulting from a multi-scale physiological system using a data-driven model includes generating a database of physiological measurements associated with a multi-scale physiological system. A computer uses dimensionality reduction techniques on the database to identify a reduced set of components explaining the multi-scale physiological system. The computer learns a data-driven model of the multi-scale physiological system from the database. Then, new input parameters are received by the computer and used to compute new physiological measurements using the data-driven model. New derived physiological indicators are computed by the computer based on the reduced set of components. Once computed, the new derived physiological indicators may be displayed along with the new physiological measurements.