Medical Data Mapping Ontology Intermediary
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Solution Overview
Problem
Heterogeneous medical terms and data formats used in individual clinics hinder the seamless transfer and utilization of medical information across different hospitals and systems, leading to inefficiencies in patient care and data exchange.
Innovation Solution
A method and system that utilize a domain clinic model ontology, SNOMED CT ontology, and vMR ontology to generate mapping files through similarity calculations and natural language processing, enabling the mapping of medical data from individual clinics to standardized formats like SNOMED CT and vMR, facilitating interoperability across different healthcare systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual clinics use local medical terms and data formats, then each clinic can customize its system to its specific needs, but medical data cannot be effectively utilized across different hospitals and systems
Solution Approach 1:
The patent introduces SNOMED CT as an intermediary ontology that mediates between local clinic ontologies and standardized medical data formats. The mapping algorithm automatically translates local terms through SNOMED CT to universal medical terminology, enabling data exchange while preserving local customization. This resolves the contradiction by using SNOMED CT as a bridge that maintains both local adaptability and global interoperability.
Solution Approach 2:
The patent segments the mapping process into multiple hierarchical levels: local clinic ontology → SNOMED CT ontology → standardized data format. This segmentation allows each layer to maintain its own characteristics while enabling translation between layers, thus preserving local customization needs while achieving standardization for data exchange.
2Ease of manufacture
If medical data is stored in heterogeneous formats across different systems, then each system can optimize for its specific requirements, but data exchange and patient transfer between systems become inefficient
Solution Approach 1:
The mapping algorithm uses SNOMED CT as an intermediary to translate between heterogeneous data formats. When data needs to be exchanged between systems, it is automatically mapped through the SNOMED CT ontology, enabling efficient data exchange without requiring systems to abandon their optimized local formats. This resolves the contradiction by maintaining system optimization while enabling productive data exchange.
3Measurement precision
If manual mapping of medical terms between systems is performed, then accuracy can be maintained, but the process becomes time-consuming and complex
Solution Approach 1:
The mapping algorithm operates autonomously to automatically generate mapping relationships between local clinic ontologies and SNOMED CT ontology. The system self-services by calculating similarities between terms, identifying mappings, and generating mapping files without requiring manual intervention. This resolves the contradiction by achieving both high accuracy through systematic comparison and time efficiency through automation.
Solution Approach 2:
The algorithm changes the parameter of mapping from manual term-by-term comparison to automated similarity-based matching. By calculating similarity parameters between terms using computational methods, the system achieves accurate mappings rapidly without manual effort, resolving the time-accuracy contradiction.
Data Source
AI summary
The present invention relates to a method for mapping heterogeneous medical data, the method comprising the steps of: generating domain clinic model ontology that defines a concept of arbitrary medical data collected from individual clinics and a relationship between the medical data; if SNOMED CT ontology that defines the concept and relationship of the domain clinic model ontology and standardized medical terms is loaded and the concept included in the domain clinic model ontology and/or the SNOMED CT ontology has a degree of similarity equal to or greater than a preset threshold value, determining that the concept has been mapped and generating a DCM-SNOMED mapping file for the mapping information; if vMR ontology describing the concept and relationship of data models defined by the SNOMED CT ontology and Health Level 7 standard is loaded and the concept included in the SNOMED CT ontology and/or the vMR ontology has a degree of similarity equal to or greater than a preset threshold value, determining that the concept has been mapped and generating a vMR-SNOMED mapping file for the mapping information; and generating a DCM-vMR mapping file for mapping information between the arbitrary medical data collected from the individual clinics and the concept included in the vMR ontology by using the DCM-SNOMED mapping file and the MR-SNOMED mapping file.