Graph Neural Network Doctor Allocation System
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Solution Overview
Problem
In complex systems like smart hospitals, existing AI technologies lack interpretability and explainability in resource allocation, making it difficult for operators to understand and trust the allocation of limited resources such as doctors and treatment rooms to patients, which is crucial for optimal management.
Innovation Solution
A method using graph neural networks to extract human-interpretable entity profiles by constructing neural network layers that model edges between entities as functions of associated text and cluster representations, learning tensors to capture patterns, and generating word-based and vector-based profiles for entities, enabling explainable resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If graph neural networks with node embeddings are used for resource allocation, then allocation accuracy and automation are improved, but interpretability and explainability deteriorate
Solution Approach 1:
The patent introduces an intermediary explanation module that mediates between the GNN allocation system and human operators. This module generates natural language explanations that translate the black-box neural network decisions into human-understandable reasoning, thereby recovering the interpretability information lost during automation without compromising the automated allocation process
Solution Approach 2:
The system segments the resource allocation process into distinct components: the GNN-based allocation engine that handles automated decision-making, and a separate explanation generation module that provides interpretability. This segmentation allows each component to specialize in its strength while the integration resolves the contradiction between automation and interpretability
2Reliability
If comprehensive entity profiles are extracted for all entities, then system management understanding is improved, but data processing complexity and time increase
Solution Approach 1:
The patent applies local quality by extracting and processing entity profiles selectively based on their relevance to the current allocation decision. Rather than uniformly processing all entity data, the system focuses computational resources on extracting profile features that are locally relevant to each specific allocation scenario, reducing overall processing complexity while maintaining management understanding
Solution Approach 2:
The system performs preliminary action by pre-extracting and storing entity profiles during idle periods or in advance of allocation decisions. This pre-processing creates a ready-to-use knowledge base of entity characteristics, reducing the complexity and time required for profile extraction during actual allocation operations
Data Source
AI summary
An allocation system includes a memory storing instructions, and a processor. The processor is configured to access the memory and execute the instructions to: obtain data that includes profiles of doctors and clinical initial information of a patient that arrives at a hospital; preprocess the obtained data, wherein preprocessing includes a text preprocessing pipeline and word embedding; allocate a doctor to the patient that arrives at the hospital by using a neural network, wherein the neural network is machine-learned by using graph data of clinical narratives composed by doctors about patients, historical profiles of the patients and the profiles of doctors; and output information indicating the allocated doctor.