Ontology-Based Data Integration for Clinical Trial Databases
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Solution Overview
Problem
Current methods for integrating clinical trial data into a database lack flexibility and efficiency, particularly when only a minor amount of data is of interest, as they require defining complex ontologies for all data sources, which is time-consuming and infeasible for users.
Innovation Solution
An ontology-based method that dynamically integrates data by creating links between selected concepts and properties of a reference ontology and data source ontologies, allowing for in-advance data integration, facilitating the assembly of data integration modules and enabling users to adapt ontological representations through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex ontologies are defined for all data sources to ensure comprehensive data integration, then data integration completeness is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the ontology definition process by introducing a reference ontology that contains only the essential concepts and properties needed for clinical trial data integration. This reference ontology is divided into core elements that can be selectively extended, rather than requiring complete ontologies for all data sources. The segmentation allows the system to focus on integrating only the necessary data elements while maintaining completeness for the intended purpose.
Solution Approach 2:
The patent applies partial action by implementing selective ontology extension, where only the specific concepts and properties relevant to a particular data source are defined and linked to the reference ontology. Instead of requiring full ontological coverage of all possible data sources, the system performs partial ontology definition tailored to the specific integration needs, reducing complexity while maintaining sufficient completeness for clinical trial data integration.
2Reliability
If complex ontologies are defined for all data sources to ensure comprehensive data integration, then data integration completeness is improved, but time consumption increases making it infeasible for users
Solution Approach 1:
The patent implements preliminary action by pre-defining a reference ontology containing the core concepts and properties required for clinical trial data integration before actual data source integration begins. This reference ontology serves as a prepared framework that eliminates the need for users to define ontologies from scratch for each data source, significantly reducing the time required for ontology definition while ensuring completeness of essential integration elements.
Solution Approach 2:
The reference ontology serves as a universal foundation that can be applied across multiple different data sources in the clinical trial domain. By creating a multi-functional reference ontology that covers common concepts and properties used in clinical trials, the system enables rapid integration of various data sources without requiring separate complete ontology definitions for each one, thereby reducing time consumption while maintaining integration completeness.
3Productivity
If selective ontology linking is implemented to reduce work, then time efficiency is improved, but data integration flexibility may be compromised
Solution Approach 1:
The patent implements dynamics by allowing the ontology structure to be flexibly extended and adapted based on specific data source requirements. The reference ontology serves as a dynamic base that can be selectively extended with additional concepts and properties as needed for different data sources. This dynamic approach enables the system to maintain high productivity through selective linking while preserving adaptability when new integration requirements emerge, as the ontology can be evolved without requiring complete redefinition.
Data Source
AI summary
A method is provided. The method comprises accessing a target database comprising at least one table associated with a first concept or property of a reference ontology, defining a data source ontology for a data source comprising a dataset, said data source ontology comprising a second concept or property, wherein said second concept or property is different from said first concept or property, and creating a link between said second concept or property and said first concept or property, said link defining to which table of said target database data of said dataset, associated with said second concept or property, is related.