Medical Data Processing via Knowledge Graph Fusion
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Solution Overview
Problem
Current medical data processing methods rely heavily on artificial processing, which is inefficient, especially in handling large volumes of data, making it difficult to enhance processing efficiency in applications like electronic medical interrogation platforms and analytical statistics.
Innovation Solution
A method that involves acquiring case-history data, generating a disease-analysis vector by creating a case-history semantic vector, determining possibility weights for preset diseases using a knowledge graph, and fusing these weights to obtain a comprehensive disease-analysis vector, incorporating both individual and common knowledge aspects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If artificial processing is used for medical data, then processing accuracy can be maintained through expert judgment, but processing efficiency is extremely low especially for large volumes of data
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary between medical data and analysis results. The knowledge graph stores structured medical knowledge including disease-symptom relationships, disease-drug relationships, and other medical entities. This intermediary enables automated processing while maintaining accuracy by grounding the analysis in established medical knowledge, thus resolving the contradiction between automation and accuracy.
Solution Approach 2:
The patent replaces the mechanical system of manual expert processing with an automated computational system. The system uses natural language processing to extract information from medical records, queries the knowledge graph for relevant medical knowledge, and automatically generates analysis results. This substitution dramatically improves processing efficiency while maintaining reliability through knowledge-based reasoning.
2Productivity
If automated processing is introduced to improve efficiency, then processing speed increases, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the medical data processing system into distinct functional modules: a knowledge graph construction module that stores medical knowledge, a natural language processing module that extracts information from medical records, a query module that retrieves relevant knowledge, and an analysis module that generates results. This segmentation makes the complex automated system more manageable and maintainable while preserving its efficiency benefits.
3Measurement precision
If comprehensive medical knowledge is integrated into the processing system, then analysis accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent extracts only the necessary medical knowledge from comprehensive medical databases and stores it in a structured knowledge graph. The knowledge graph contains specifically curated relationships such as disease-symptom associations, disease-drug relationships, and other clinically relevant entities. This extraction approach maintains high analysis accuracy by including essential medical knowledge while reducing processing complexity by excluding unnecessary comprehensive data.
Data Source
AI summary
A method for processing medical data, an apparatus and a storage medium, the method includes: acquiring a case-history datum, and performing a target process to obtain a disease-analysis vector, wherein the target process includes: generating a case-history semantic vector of the case-history datum; for each of preset diseases in a preset-disease set, determining a first possibility weight of the case-history datum caused by the preset disease according to the case-history semantic vector, to obtain a first weight vector; according to case-history symptoms and case-history diseases in the case-history datum, determining from a predetermined knowledge graph a candidate disease that is capable of generating generate the case-history datum; determining a second possibility weight of the case-history datum caused by the candidate disease, to obtain a second weight vector; and fusing the first weight vector and the second weight vector, to obtain the disease-analysis vector corresponding to the case-history datum.