Graph Embedding for Healthcare Provider Matching Accuracy
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Solution Overview
Problem
Existing healthcare systems lack an efficient method to predict and recommend suitable healthcare facilities or providers for specific medical procedures based on complex relationships between various healthcare entities, leading to suboptimal decision-making and resource utilization.
Innovation Solution
A graph-based framework using graph embedding and machine learning to analyze healthcare data, transforming it into vectors to capture relationships between patients, providers, and facilities, enabling probability predictions and recommendations for medical procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph-based framework with embedding is applied to capture complex relationships between healthcare entities, then measurement precision and analysis accuracy are improved, but device complexity and data processing requirements increase
Solution Approach 1:
Graph embedding serves as an intermediary technique that transforms complex graph data into lower-dimensional vector representations. This mediator converts the complex relationship structure into a form that machine learning models can efficiently process, resolving the contradiction by introducing a transformation layer that preserves relationship information while reducing computational complexity.
Solution Approach 2:
The patent transforms the data representation parameters by converting high-dimensional graph structures into lower-dimensional embedding vectors. This parameter change maintains the essential relationship information while reducing the dimensionality and complexity of the data, enabling more efficient processing without sacrificing measurement precision.
2Productivity
If massive healthcare data is processed and stored to enable data-driven analysis, then productivity and decision-making quality are improved, but loss of time for data storage and organization increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and embedding healthcare data into graph representations in advance. This preliminary transformation organizes the data structure and relationships beforehand, so that when analysis is needed, the pre-organized graph embeddings can be quickly queried and processed, reducing the time required for data organization during actual decision-making processes.
3Device complexity
If graph embedding transforms complex graph data model into lower dimensional space, then device complexity is reduced and processing efficiency is improved, but loss of information may occur during transformation
Solution Approach 1:
Graph embedding changes the dimensional parameters of the data representation while preserving the essential structural information through learned transformations. The embedding process optimizes the transformation to maintain relationship patterns and topological properties in the lower-dimensional space, minimizing information loss while achieving dimensionality reduction.
Data Source
AI summary
A system and method for using a graph-based data structure to capture complex relations between different healthcare entities, analyze and mine healthcare data. The system sets up a framework for analyzing and mining historical healthcare data to help clinical patients and practitioners to guide care and make early decisions for interventions. More particularly, the system and method use graph embedding and machine learning modeling to process healthcare data in order to match member/patients with healthcare facilities or providers for performing a particular medical procedure or other health intervention needed by the patient.