Graph Embedding for Healthcare Facility Recommendation Accuracy
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Solution Overview
Problem
Current healthcare data management systems lack an effective method to capture and analyze complex relationships between healthcare entities, such as patients, providers, and facilities, which hinders data-driven decision-making and efficient resource allocation.
Innovation Solution
A graph-based framework using graph embedding and machine learning is employed to process healthcare data, creating a system that predicts and recommends suitable healthcare facilities for specific medical procedures by transforming complex data into vector representations, enabling better decision-making through data-driven analysis.
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 reliability of healthcare facility recommendations are improved, but device complexity and difficulty of detecting and measuring relationships increase
Solution Approach 1:
The patent introduces graph embedding as an intermediary technique that transforms complex graph data representing healthcare entity relationships into vector representations. This intermediary step simplifies the complex relationships into a format that machine learning models can process efficiently, thereby improving recommendation accuracy without proportionally increasing system complexity
Solution Approach 2:
The patent replaces traditional rule-based or simple statistical methods (mechanical systems) with graph embedding and machine learning models. This substitution enables the system to automatically learn and capture complex relationships between healthcare entities, providers, and facilities, significantly improving measurement precision and recommendation reliability
2Productivity
If graph embedding and machine learning are used to process healthcare data, then productivity and effectiveness of data-driven analysis are improved, but loss of time for data processing and model training increases
Solution Approach 1:
The patent applies graph embedding techniques to pre-process and transform healthcare data into vector representations before feeding them into machine learning models. This preliminary action organizes complex relationship data into a standardized format, reducing the computational burden during model training and inference, thereby improving overall productivity while managing processing time
Solution Approach 2:
The patent transforms graph data into vector space through embedding, fundamentally changing the parameter representation from complex graph structures to compact numerical vectors. This parameter transformation enables efficient processing by machine learning algorithms, significantly improving productivity in data-driven healthcare analysis
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 for performing a particular medical procedure needed by the patient.