Graph-Based Predictive Classifications for Entity Taxonomies
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Solution Overview
Problem
Conventional classification techniques are prone to misclassification errors due to their broad taxonomies and inability to account for high variance and heterogeneity in predictive domains, such as clinical procedures, leading to information value degradation.
Innovation Solution
The implementation of graph-based approaches to generate empirical domain taxonomies by breaking down entity classes into subclasses based on homogeneous behavior, using network graphs with node and edge weights to create granular predictive classifications tailored to actual entity interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional broad taxonomies are used for classification, then the classification system is simple and easy to operate, but misclassification errors increase and information value degrades
Solution Approach 1:
The patent segments broad entity classes into multiple homogeneous subclasses based on behavioral patterns. For example, instead of classifying all clinical providers under a single taxonomy code, the system divides them into subclasses based on their interaction patterns with patients, procedures, and medications. This segmentation maintains operational simplicity while significantly improving classification accuracy by reducing misclassification errors.
Solution Approach 2:
The patent applies local quality by creating customized classification criteria for different subclasses within the same entity class. Each subclass is defined by specific behavioral characteristics relevant to that subgroup, rather than applying a uniform classification rule to all entities. This allows the system to capture heterogeneity and reduce information value degradation while maintaining overall system simplicity.
2Device complexity
If traditional taxonomies are used, then the classification structure is straightforward, but the system cannot account for high variance and heterogeneity in predictive domains
Solution Approach 1:
The patent introduces dynamics by making the taxonomy adaptive and evolving based on observed behavioral patterns. Rather than using static, predetermined taxonomies, the system dynamically creates and updates subclasses based on actual entity interactions and behaviors. This allows the classification system to adapt to high variance and heterogeneity in predictive domains while maintaining a straightforward overall structure.
Solution Approach 2:
The patent implements self-service by enabling the classification system to automatically generate and refine its own taxonomy structure based on observed data patterns. The system autonomously identifies homogeneous behavior patterns and creates appropriate subclasses without requiring manual reconfiguration, thereby handling variance and heterogeneity while keeping the taxonomy structure manageable.
3Ease of operation
If self-designated taxonomy codes are used by entities, then the classification process is simple, but misclassification errors occur due to incorrect self-designation
Solution Approach 1:
The patent applies feedback by using observed behavioral patterns to verify and correct self-designated taxonomy codes. The system monitors entity interactions and compares actual behavior against declared taxonomy classifications, then adjusts classifications accordingly. This feedback mechanism maintains the simplicity of self-designation while significantly improving reliability by reducing misclassification errors through automated verification.
Data Source
AI summary
Various embodiments of the present disclosure provide graph-based techniques for generating granular predictive classifications for entities in a predictive domain. The graph-based techniques include generating a network graph for an entity or entity class based on a plurality of interaction data objects for the entity. The network graph includes a plurality of nodes and a plurality of edges. Each node corresponds to a particular interaction code of at least one of the plurality of interaction data objects. Each edge connects a node pair that is associated with a particular interaction data object. The nodes and edges are weighted to enable the clustering of the network graph for an entity class. An entity network graph may be compared to node clusters of a class network graph to generate a behavior based predictive classification.