# Semi-Automated Mapping of German Study Data Concepts to an English Common Data Model
## 4 Discussion  
We present an approach for mapping nonstandardized German medical data to the OMOF common data model. The established workflow handles German data from translation to concept suggestion without the need for human-computer interaction. This can be of advantage particularly when dealing with large datasets and frequent mapping tasks. The performance of our algorithm is comparable to the manual method, in that it suggests the right OMOF concept as frequently.  
However, there is still room for improvement: For one, a major challenge lies in the correct translation of clinical expressions, especially from unstructured data. Here, a great need exists for a comprehensive multilingual medical text corpus as the basis for improved language models. Although some efforts are being made in individual countries, there seems to be the need for a concerted effort to include a comprehensive collection of languages in this corpus. For example, a project called the European Clinical Case Corpus (E3C, [[https://e3c.fbk.eu](https://e3c.fbk.eu)]([https://e3c.fbk.eu](https://e3c.fbk.eu))) has generated a freely available multilingual corpus in English, French, Italian, Spanish, and Basque but is not applicable to German texts. And, German efforts to create a medical text corpus for natural language processing as part of the Medical Informatics Initiative are currently predominantly limited to the German language itself.  
Additional to the translation of non-English terms to English, the project has highlighted problems with the identification of word synonyms. For example, the algorithm struggles with the equivalence of verbs, nouns, and adjectives of the same word stem (larynx vs. laryngeal; tracheotomize vs. tracheotomy), as well as words with similar meaning (injury vs. trauma). To improve results, a semantic web with word embeddings  
Figure 4: Example of the Floating-Action-Button graphical user interface (FAB-GUI). **Left**: first page with tabular view of mappable items. **Middle**: second page with mapping suggestions for a selected item. **Right**: second page with expanded dataset description.  
representing semantic distances could be utilized which would account for such semantic similarities [32; 33; 34].  
Furthermore, the comprehensiveness of concept synonyms within the OMOP catalogue could be reviewed. Especially for standard medical abbreviations, shortcomings have become apparent. However, abbreviations are inherently ambiguous. For example, the abbreviation CO can stand for carbon monoxide or cardiac output, and PR may mean progesterone receptor or pulse rate. Therefore, it seems that some form of integration of domain knowledge is inevitable. For example, a large dictionary of 858 thousand medical acronyms and abbreviations [35] could be integrated in the algorithm, combined with the need for user-computer interaction to select the domain-specific appropriate long name.  
Problems also arise from the words and phrases chosen in these unstructured datasets, with the use of nonstandard expressions and layman's terms affecting translation and subsequent mapping. Also, the relation between individual variables (such as questions within a questionnaire) has an impact. As each variable name is processed independently, a semantic reference of a question to the one before needs to be avoided under all circumstances. Therefore, the early involvement of a domain expert for data standardization during study design should be considered.  
Overall, the presented mapping tool shows a feasible approach for the automation of specific mapping tasks. Its code is publicly available and customizable, and could be integrated into a metadata repository. Besides a user-friendly graphical user interface, additional functions could be added, such as the ability to select specific OMOP vocabularies or domains.