Root Cause Coding Using Feature Contribution Drift Analysis
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Solution Overview
Problem
Conventional root cause analysis techniques fail to adequately identify the specific contributions of different variables, leading to incomplete understanding and ineffective solutions, particularly in sectors like healthcare where factors such as staff training and system design flaws are overlooked.
Innovation Solution
A system that utilizes machine learning models and model interpretability methods to generate matrices of feature contributions and partial dependence plots, enabling the identification of principal drivers of change due to data or concept drift by analyzing baseline and updated datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional root cause analysis techniques are used, then the analysis process is simple, but the identification of specific variable contributions is inadequate
Solution Approach 1:
The patent introduces model interpretability methods (such as SHAP, LIME, or permutation importance) as intermediary tools between the machine learning model and the root cause analysis process. These methods generate feature contribution matrices that quantify the specific impact of each variable on model predictions, enabling precise identification of contributing factors without requiring changes to the underlying ML model architecture.
Solution Approach 2:
The patent segments the root cause analysis into distinct computational components: (1) generating feature contribution matrices for baseline and updated datasets separately, (2) computing the difference between these matrices to identify shifts, and (3) ranking variables based on the magnitude of change. This segmentation allows for systematic and precise measurement of variable contributions while maintaining computational efficiency.
2Loss of information
If conventional root cause analysis techniques are used, then the implementation is straightforward, but the understanding of root causes is incomplete
Solution Approach 1:
The patent implements a feedback mechanism where the system generates feature contribution matrices, computes their differences, and uses the results to identify principal drivers of change. This feedback loop provides comprehensive information about which variables are responsible for shifts in model predictions, enabling complete understanding of root causes while maintaining a systematic analysis process.
Solution Approach 2:
The patent changes the analytical parameters from simple presence/absence of variables to quantitative feature contribution values. By computing the difference in contribution values between baseline and updated datasets, the system identifies variables whose impact has changed most significantly, providing complete information about root causes without requiring complex additional analysis.
3Measurement precision
If detailed feature contribution analysis is performed, then the identification of principal drivers is precise, but the computational processing is more intensive
Solution Approach 1:
The patent applies partial action by focusing computational resources on computing feature contribution matrices only for the baseline and updated datasets, rather than analyzing all possible variable interactions. The difference computation then naturally highlights the most significant changes, providing precise identification of principal drivers with moderate computational overhead.
Data Source
AI summary
Methods and systems are described herein for identifying one or more variables as key drivers of shift in a value of interest. In some aspects, a root cause analysis system may implement a subroutine to use feature contributions to identify the contributions of each variable in a baseline dataset and an updated dataset, where a value of interest has shifted between the baseline and updated datasets. By taking the difference in feature contributions between the datasets, the system may identify those variables with the largest shift in contribution as principal drivers of change for a value of interest. In some aspects, a root cause analysis system may implement a subroutine to generate partial dependence plots (PDPs) for each feature. By comparing different PDPs for each feature, the system may identify the features with significantly different feature-target relationships and find the key segments responsible for performance change.