Medical Knowledge Graph Interaction for Retrieval-Augmented Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional solutions for accessing and interacting with rapidly expanding medical knowledge are inefficient, inconvenient, and often fail to provide timely access to the latest, trustworthy information due to slow processing and inadequate filtering.
Innovation Solution
A hybrid knowledge graph-large language model (KG-LLM) system that integrates a retrieval-augmented generation model to facilitate efficient interaction with medical knowledge, enabling robust retrieval and organization of complex information from multiple sources while minimizing hallucinations and requiring minimal supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional solutions are used to access medical knowledge, then information can be retrieved, but the process is prohibitively slow and inefficient
Solution Approach 1:
The patent segments medical knowledge into structured knowledge graphs with hierarchical categories (e.g., diseases, treatments, symptoms as separate nodes), enabling parallel processing and faster retrieval compared to conventional linear search methods
Solution Approach 2:
The system pre-processes and organizes medical information into knowledge graphs before queries are submitted, creating indexed relationships between medical concepts in advance, which dramatically accelerates retrieval speed when users search for specific information
2Loss of information
If comprehensive medical knowledge is collected, then information completeness improves, but processing complexity and time increase
Solution Approach 1:
The patent divides comprehensive medical knowledge into modular knowledge graphs with distinct domains (e.g., oncology, cardiology) and hierarchical structures, allowing the system to manage complexity by processing only relevant segments rather than the entire knowledge base
Solution Approach 2:
The system introduces knowledge graphs as an intermediary layer between raw medical data and user queries, transforming unstructured comprehensive information into structured relationships that are easier to process and query efficiently
3Ease of operation
If conventional user interfaces are used, then basic functionality is provided, but user interaction efficiency and ease of use deteriorate
Solution Approach 1:
The patent transforms traditional linear text-based search interfaces into multi-dimensional knowledge graph visualizations where users can navigate through hierarchical categories, filter by multiple criteria simultaneously, and explore relationships between medical concepts through graphical interactions
Data Source
AI summary
A method including: displaying, on a graphical user interface, a knowledge graph associated with a domain, wherein the knowledge graph includes a number of nodes and a number of edges representing relationships between the number of nodes, wherein the number of nodes include a number of leaf nodes, each of the number of leaf nodes being associated with respective metadata related to the domain; receiving, at the graphical user interface, one or more user inputs, wherein the one or more user inputs include a selection of a specific leaf node of the number of leaf nodes; displaying, on the graphical user interface, the respective metadata related to the domain that is associated with the specific leaf node; and providing, on the graphical user interface, a search window configured to receive a search query related to the domain.