Multiplex HCP Community Detection Using Graph Convolution
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Solution Overview
Problem
Conventional approaches fail to comprehensively identify collaborative communities of healthcare providers (HCPs) due to disjunctive and disparate network information, high dimensionality, and computational complexity, and do not account for multiple types of relationships among HCPs.
Innovation Solution
A Multiplex Graph Convolutional Network (MGCN) artificial intelligence machine learning approach is used to analyze multiple types of relationships among HCPs, constructing multiplex graphs and optimizing community detection through joint optimization and dimensionality reduction to identify collaborative communities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional approaches analyze single relationships at a time, then analysis simplicity is maintained, but comprehensive community identification capability deteriorates
Solution Approach 1:
The patent merges multiple disparate relationship networks (referral networks, co-treatment networks, communication networks, etc.) into a unified multiplex network structure. This allows simultaneous analysis of multiple relationship types, resolving the contradiction by combining simplicity of single-network analysis with comprehensiveness of multi-network analysis through the multiplex framework that integrates various HCP relationships while maintaining manageable computational structure
Solution Approach 2:
The multiplex network framework serves multiple functions simultaneously: it captures diverse relationship types, enables community detection across different relationship dimensions, and provides a unified analysis platform. This multi-functionality allows the system to maintain analytical simplicity while achieving comprehensive community identification across referral, co-treatment, communication, and other HCP relationships
2Measurement precision
If multiple types of relationships among HCPs are analyzed, then community detection accuracy is improved, but computational demand increases
Solution Approach 1:
The patent segments the complex multiplex network analysis into distinct relationship types (referral relationships, co-treatment relationships, communication relationships, etc.), each represented as separate networks within the multiplex structure. This segmentation allows the system to process multiple relationship types efficiently by treating them as modular components, reducing overall computational demand while maintaining accurate community detection across all relationship dimensions
Solution Approach 2:
The patent transitions from analyzing single relationship networks to a multiplex network structure that adds a network-type dimension. This dimensional change allows simultaneous representation of multiple relationship types without proportionally increasing computational complexity, as the multiplex framework provides an efficient mathematical structure for handling multi-relational data that scales better than analyzing each relationship type separately
3Loss of information
If comprehensive network data is collected from multiple sources, then community identification completeness is improved, but data integration complexity increases
Solution Approach 1:
The multiplex network structure serves as an intermediary framework that integrates disparate network data from multiple sources (referral networks, co-treatment networks, communication networks, etc.). This intermediary structure unifies diverse data types into a consistent mathematical representation, reducing integration complexity while preserving the completeness of network information across all HCP relationship types
Data Source
AI summary
Methods and systems to identify collaborative communities of individuals from graphs of multiple types of relationships amongst the individuals, including to mine data related to multiple types of relationships amongst individuals, construct graphs to represent the respective types of relationships amongst individuals, and perform a multiplex graph convolutional network (MGCN) artificial intelligence machine learning (AIML) analysis across the multiple graphs to identify the collaborative communities. A mathematical representation of the graphs may be learned/tuned to optimize clustering of the individuals. Multiple parameters (inter-graph weights, consensus regularization function) may be jointly tuned based on a joint optimization function. The collaborative communities may be displayed such that relative positions of the individuals represent measures of influence exerted by the respective individuals within the respective collaborative communities.