Shapley Value Interpretation for Molecular Diagnostic Classifiers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models in clinical diagnostics lack transparency and computational feasibility in calculating Shapley values due to exponential complexity and the need for retraining when evaluating attribute subsets, especially with large numbers of attributes.
Innovation Solution
The Diagnostic Cortex architecture, which combines atomic classifiers with dropout regularization, allows for efficient calculation of Shapley values using Monte Carlo-based sampling without retraining, enabling polynomial-time evaluation even with hundreds or thousands of attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used in clinical diagnostics, then predictive accuracy can be high, but transparency and explainability are lost
Solution Approach 1:
The patent segments the machine learning model into atomic classifiers that process individual attributes separately, allowing the system to maintain high predictive accuracy while providing transparent explanations through Shapley values that attribute contributions to specific segments (attributes)
Solution Approach 2:
The patent introduces Shapley values as an intermediary mechanism between the black-box ML model and interpretable explanations, mediating the information flow to provide both accuracy and transparency without requiring model simplification
2Loss of information
If Shapley values are calculated for all possible attribute subsets to provide explainability, then transparency is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent applies partial action by calculating Shapley values only for attribute subsets that are computationally feasible rather than all possible subsets, using Monte Carlo sampling to approximate Shapley values for large numbers of attributes while maintaining explainability
Solution Approach 2:
The patent uses Monte Carlo sampling to create virtual copies of attribute subsets for calculating Shapley values, allowing the system to estimate contributions without exhaustively evaluating all possible combinations, thus reducing computational complexity
3Measurement precision
If the number of attributes increases to improve diagnostic accuracy, then predictive performance improves, but the feasibility of calculating Shapley values decreases
Solution Approach 1:
The patent changes the computational parameters by using Monte Carlo sampling methods that scale linearly with the number of attributes rather than exponentially, allowing Shapley value calculation to remain feasible even as the number of attributes increases to improve diagnostic accuracy
Data Source
AI summary
Shapley values (SVs) have become an important tool to further the goal of explainability of machine learning (ML) models. However, the computational load of exact SV calculations increases exponentially with the number of attributes. Hence, the calculation of SVs for models incorporating large numbers of interpretable attributes is problematic. Molecular diagnostic tests typically seek to leverage information from hundreds or thousands of attributes, often using training sets with fewer instances. Methods are described for evaluate SVs using Monte Carlo sampling or exact calculation in polynomial time (i.e., reasonably quickly and efficiently) using the architecture of a ML model designed for robust molecular test generation, and without requiring classifier retraining.