Rule Induction for Human-Readable Pattern Discovery in Diagnostic Data
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Solution Overview
Problem
Traditional datamining workflows for analyzing large diagnostic datasets are slow and prone to missing important patterns, particularly those with small but disproportionate impact, due to the high dimensionality and complexity of the data, and the manual approach often fails to uncover significant subsets effectively.
Innovation Solution
A modified rule induction algorithm using a separate-and-conquer approach combined with a greedy heuristic and beam search, which prioritizes simple and understandable rules, automatically discovers patterns in datasets by optimizing for specific target values and generating human-readable rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual data mining workflow is used to analyze large diagnostic datasets, then the analysis can be performed with simple tools, but the process is slow and prone to missing important patterns
Solution Approach 1:
The patent replaces manual mechanical data exploration with an automated machine learning system that uses rule induction algorithms to automatically discover patterns. The system substitutes human analysts' manual pivoting and filtering with automated computational methods that can systematically evaluate all possible patterns without fatigue or oversight.
Solution Approach 2:
The patent introduces rule induction algorithms as an intermediary between raw diagnostic data and human analysis. These algorithms generate human-readable rules that bridge the gap between complex computational patterns and human understanding, allowing automated processing while maintaining interpretability for human reviewers.
2Ease of operation
If investigation begins with the largest group of records to improve reliability score, then the approach is easy and straightforward, but it fails to uncover small subsets with disproportionate impact
Solution Approach 1:
The patent applies partial action by allowing users to specify minimum coverage thresholds for pattern discovery. Instead of requiring complete exploration of all possible patterns, the system efficiently identifies patterns that meet predetermined significance criteria, performing sufficient analysis without exhaustive enumeration of all possibilities.
Solution Approach 2:
The patent changes the parameter of pattern significance from size-based (largest groups first) to impact-based (patterns with disproportionate effect on reliability). The rule induction algorithm evaluates patterns based on their contribution to the target measure rather than their raw size, enabling detection of small but critical patterns.
3Quantity of substance
If the number of dimensions in the dataset is large, then comprehensive data collection is achieved, but practical exploration becomes infeasible
Solution Approach 1:
The patent segments the high-dimensional data exploration task into manageable components through rule induction. Instead of requiring users to manually navigate the full dimensional space, the algorithm automatically generates rules that represent meaningful segments or subsets of the data, breaking down the overwhelming complexity into interpretable discrete patterns.
Solution Approach 2:
The patent transforms the problem from exploring high-dimensional space directly to operating in the space of rules and patterns. By inducing rules that capture relationships across dimensions, the system effectively adds an abstraction dimension that simplifies navigation of the underlying high-dimensional data structure.
Data Source
AI summary
Rule induction is used to produce human readable descriptions of patterns within a dataset. A rule induction algorithm or classifier is a type supervised machine learning classification algorithm. A rule induction classifier is trained, which involves using labelled examples in the dataset to produce a set of rules. Rather than using the rules/classifier to make predictions on new unlabeled samples, the training of the rule induction model outputs human-readable descriptions of patterns (rules) within the dataset that gave rise to the rules (rather than using the rules to predict new unlabeled samples). Parameters of the rule induction algorithm are tuned to favor simple and understandable rules, instead of only tuning for predictive accuracy. The learned set of rules are outputted during the training process in a human-friendly format.