Auditable Variant Interpretation Platform Using Blockchain Evidence Models
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Solution Overview
Problem
Current variant interpretation support systems face challenges in reliably determining the phenotypic impact of molecular variants due to high numbers of variants of unknown significance, inconsistent and evolving knowledge, and the lack of uniform truth sets, leading to outdated and conflicting evidence models that complicate the evaluation and comparison of performance metrics.
Innovation Solution
A computer-implemented method that records evidence models, evaluates their performance, generates hash values for auditing, and ranks them based on validation and test performance data to provide the best-performing model for predicting phenotypic impacts, using machine learning techniques and a distributed data structure like blockchain for auditing and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple evidence models are used to interpret molecular variants, then the coverage of variant interpretation is improved, but the complexity of evaluating and comparing performance metrics increases
Solution Approach 1:
The patent applies homogeneity by establishing a uniform evaluation framework that standardizes performance metrics across all evidence models. This allows for consistent comparison of diverse models using common criteria such as accuracy, precision, recall, and F1 score, thereby reducing the complexity of evaluating multiple models while maintaining comprehensive variant interpretation coverage
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting performance thresholds and weighting parameters based on variant characteristics and evidence model performance. This enables the system to optimize the combination of multiple evidence models for different variant types, improving interpretative coverage while managing evaluation complexity through adaptive parameter tuning
2Measurement precision
If evidence models are updated continuously to reflect evolving knowledge, then the accuracy of phenotypic impact predictions is improved, but the stability of classification systems deteriorates
Solution Approach 1:
The patent applies preliminary action by implementing rigorous validation and testing protocols before updating evidence models with new knowledge. This ensures that only well-validated updates are incorporated, maintaining classification stability while progressively improving prediction accuracy through controlled knowledge integration
Solution Approach 2:
The patent utilizes feedback mechanisms by continuously monitoring classification consistency and prediction accuracy across updates. When instability is detected, the system adjusts update frequencies or triggers re-validation, thereby maintaining stability while allowing continuous improvement of phenotypic impact predictions through controlled evolution of evidence models
3Reliability
If comprehensive validation and testing of evidence models is performed, then the reliability of variant interpretation is improved, but the time required for model evaluation increases
Solution Approach 1:
The patent applies partial action by implementing tiered validation strategies where critical evidence models undergo comprehensive validation while less critical models receive streamlined evaluation. This selective approach maintains high reliability for essential variant interpretations while reducing overall evaluation time through prioritized validation workflows
Solution Approach 2:
The patent utilizes preliminary action by performing initial screening validations before full-scale testing. This preliminary filtering identifies models that require comprehensive validation versus those that can be quickly approved, thereby reducing total evaluation time while maintaining reliability through targeted thorough validation of critical models
4Reliability
If audit trails are implemented for all evidence models, then the transparency and trustworthiness of variant interpretation is improved, but the complexity of data management increases
Solution Approach 1:
The patent applies universality by implementing a standardized audit trail system that serves multiple functions simultaneously: tracking model versions, recording performance metrics, documenting validation results, and maintaining provenance information. This multi-functional audit infrastructure improves transparency and trustworthiness while avoiding the complexity of multiple separate tracking systems
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for optimizing the determination of a phenotypic impact of a molecular variant identified in molecular tests, samples, or reports of subjects by way of regularly incorporating, updating, monitoring, validating, selecting, and auditing the best-performing evidence models for the interpretation of molecular variants across a plurality of evidence classes.