Improving Biomedical Data Interoperability with Mapping Algorithms
Author Information
Author(s): Isaac Kunz, Ming-Chin Lin, Lewis Frey
Primary Institution: University of Utah
Hypothesis
Can interoperability across biomedical databases be improved by utilizing a repository of Common Data Elements and lexical algorithms?
Conclusion
The study demonstrates that mapping algorithms can effectively reduce the cost and time required to align local data models with reference models in biomedical informatics.
Supporting Evidence
- The algorithms showed similar performance in mapping UML models to CDEs.
- The top-ranked matches for both algorithms contained a high percentage of correct mappings.
- Automated mapping can significantly reduce the manual effort required for data integration.
Takeaway
This study shows that we can use smart computer programs to help connect different databases in medicine, making it easier for researchers to share and use data.
Methodology
The study compared the performance of two algorithms (Dice and Dynamic) in mapping UML model class-attributes to Common Data Elements (CDEs) using similarity measures.
Limitations
The algorithms may struggle with synonyms and abbreviations, which can affect mapping accuracy.
Digital Object Identifier (DOI)
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