# Semi-Automated Mapping of German Study Data Concepts to an English Common Data Model
## 3 Results
### Application to Independent Datasets  
In order to evaluate the performance of our algorithm when mapping other datasets, we applied the refined code to a small dataset of variable names from an anesthesiology study as well as to abbreviated variables from anesthesiology measurements. The first set of study variables is used to measure the direct applicability of our code to other datasets of similar structure. The abbreviated set was chosen for one to evaluate the mappability of abbreviations (mostly via synonyms), as well as the use of an expanded list of OMOF concepts including both SNOMED CT and LOINC terms and their effect on mapping performance.  
The first task of providing OMOF concept suggestions for anesthesiology study names was similar to the task the algorithm was designed for. However, some of the medical terms in this methodological study may be more uncommon than those in the HCHS public health cohort study. Here, the algorithm was able to find a suitable OMOF concept in five of ten cases (Supplementary Table S5). In one case, ambiguous spelling of the term causes failure, as 'Thyreometanler Abstand' translates to 'thyreometal distance' but not 'thyromental distance' for concept 4142891. In another case, the term 'Maskenbeantung unmoglich' translates to'mask breathe impossible', which relates to the concept 'controlled ventilation' (4074665). This logical relation is, however, not known by the algorithm. The term 'Kehlkopf/Atenwegstrauma' which translates to 'Iarynx/respiratory trauma' should be associated with the concept 'injury of larynx' (4053585, synonym: laryngeal trauma). However, it does not appear in the algorithm's top five concept suggestions, probably because of the differing word combination of the synonymous words larynx/laryngeal and trauma/injury. In another case, the translated verb 'tracheotomize' does not lead to an association with the noun tracheotomy for which multiple potentially suitable concepts exist ('incision of trachea' (4168133), 'Tracheostomy, emergency procedure by transtracheal approach' (4208093). In one case ('Simplified Airway Risk Index'), no fitting OMOF concept could be identified.  
Figure 3: Distribution of top similarity scores of suggested OMOF concepts for all HCHS variable names. Variable names from the HCHS examinations dataset (**left**) map better based on keyword to concept similarity score than those from the HCHS questionnaires dataset (**right**). (\(p\)-value = \(1.717\times 10^{-11}\), Welch Two Sample \(t\)-test).  
When comparing the algorithm performance using an OMOP concept library only containing LOINC terms or only SNOMED CT terms or a combined library, no negative effects when using the combined library could be observed.  
The second task using anesthesiology-related measurements illustrated the challenge of mapping abbreviated medical terms to concepts (Supplementary Table S6). Here, seven of ten abbreviations failed to map to the correct concept, as these abbreviations neither occur in the OMOP concept name nor in its concept synonyms. In contrast, when using the correct long names as provided by a medical domain expert all of these standard measurements could be mapped correctly.