Mapping between clinical and preclinical terminologies: eTRANSAFE's Rosetta stone approach

Area of Science:

• Biomedical Informatics
• Translational Research
• Data Integration

Background:

• Translational research requires integrating preclinical and clinical data, often hindered by disparate terminologies and granularities.
• The eTRANSAFE project aimed to develop tools for this integration challenge.
• Existing data formats include single pre-coordinated clinical concepts and complex preclinical concept combinations.

Purpose of the Study:

• To develop and evaluate the Rosetta Stone approach for mapping preclinical concepts to clinical concepts.
• To enable flexible mapping with varying levels of exactness between different data types.
• To facilitate the combination of diverse preclinical and clinical data for research.

Main Methods:

• Concepts from preclinical (Histopathology, SEND, Mouse Gross Anatomy) and clinical (MedDRA) terminologies were mapped to SNOMED CT as an intermediary.
• Manual creation of mappings from preclinical to SNOMED CT, leveraging existing clinical to SNOMED CT mappings.
• Development of a coordination template to define mapping relations and assign inexactness penalty scores.
• Evaluation of semantic mapping against lexical term matching using a subset of 60 concepts.

Main Results:

• Over 34,000 concepts were mapped to SNOMED CT.
• A terminology service was created to dynamically provide exact and inexact mappings.
• The Rosetta Stone approach achieved 95% precision in mapping, significantly outperforming lexical matching (22%).

Conclusions:

• The Rosetta Stone approach successfully utilizes SNOMED CT to bridge preclinical and clinical data.
• The ability to generate both exact and inexact mappings increases the volume of connectable data.
• This semantic mapping strategy is valuable for advancing translational research use cases.