Diagnostic Prediction Model Distribution via Genomic Adaptation Factors
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Solution Overview
Problem
The transfer of diagnostic prediction models between laboratories or entities is hindered by data privacy concerns and the need to share patient-level training data, leading to accuracy issues and increased costs due to the requirement of trusted third-party brokers.
Innovation Solution
A computing system that distributes diagnostic prediction models by providing adaptation factors and data transformers to transform target genomic datasets to conform to the nature of the training dataset, allowing verification before purchase without sharing actual genomic data, thus enabling privacy-preserving model distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patient-level training data is shared between model provider and model consumer to ensure model accuracy and proper functioning, then model transferability and accuracy are improved, but data privacy and security are compromised
Solution Approach 1:
The patent segments the training data into aggregated statistical features (e.g., mean, variance, distribution characteristics) rather than sharing individual patient records. This allows the model consumer to understand data characteristics without accessing sensitive information, resolving the contradiction between model accuracy and data privacy.
Solution Approach 2:
The patent creates a synthetic copy of the training data's statistical properties through adaptation factors that mimic the original data distribution without replicating actual patient data. This enables model verification and accuracy assessment while preserving privacy by using artificial representations rather than real data.
2Adaptability or versatility
If a trusted third-party broker is used to facilitate model exchange and data sharing, then model transferability is improved, but system complexity and costs increase
Solution Approach 1:
The patent extracts the essential data characteristics needed for model transferability (aggregated statistics, adaptation factors) and separates them from the actual patient data and the broker infrastructure. This eliminates the need for complex trusted third-party systems while maintaining model transferability through direct provider-consumer interaction.
Solution Approach 2:
The patent introduces adaptation factors as a lightweight intermediary mechanism that enables direct model transfer between provider and consumer without requiring a trusted broker. These factors act as simple transformation parameters that bridge data differences while avoiding the complexity of broker-based verification systems.
3Measurement precision
If genomic data is transformed to correct batch effects using target dataset, then model accuracy on target data is improved, but the need for target dataset access increases data sharing requirements
Solution Approach 1:
The patent performs preliminary computation of adaptation factors using only aggregated statistics from the target dataset rather than requiring access to individual patient records. This preliminary transformation preparation enables batch effect correction while minimizing data sharing, as the statistical aggregates contain minimal sensitive information.
Data Source
AI summary
Systems and methods for distributing diagnostic prediction models. The system may receive, from an application provider, one or more diagnostic prediction models, each model trained to generate a respective diagnostic prediction based upon genomic data. Adaptation factors may be used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the model. The system may display via a graphical user interface, one or more representations corresponding to the diagnostic prediction models, and verify a diagnostic prediction model for an application consumer based upon receiving information corresponding to an application consumer genomic dataset. The system may authorize the diagnostic prediction model for distribution to the application consumer, and provide the diagnostic prediction model and the corresponding adaptation factors to the application consumer.