# Semi-Automated Mapping of German Study Data Concepts to an English Common Data Model
## 3 Results
### Code Refinement  
The initially created workflow performed inferiorly to the manual method using USAGI. Especially, medical synonyms such as 'coronary arteriosclerosis' instead of 'coronary artery disease' were not recognized.  
To refine the mapping algorithm to improve suggestion results, we selected a small set of variable names to evaluate results in detail and modify code to improve them. The original code (ORI) underwent three rounds of refinement: Firstly, we applied minor changes to cosine similarity score calculation and package use (MIN). Thereafter, a major change was applied to also use OMOP concept synonyms for term matching (SYN). Finally, duplicate concepts from synonym matching were removed from the list of results (DUP).  
A comparison of the final results from our algorithm with the standard USAGI method showed comparable results in 12 of 20 cases. In 4 of 20 cases it delivered improved results; in 2 of 20 cases the results were worse. In 2 of 20 cases the translation itself failed to return meaningful expressions which could then be mapped to OMOP concepts.  
The different cases are represented in Table 1. The identified concepts are color-coded qualitatively based on if they are a good match to the German source term (dark green--good match, light green--acceptable match, orange--poor match, red--no match). For a full list of results for all 20 terms see Supplementary Table S3.  
For a thorough evaluation of our refined mapping algorithm, we applied it to all HCHS examination and questionnaire variable names (Figure 3, Supplementary Table S4). When considering the top similarity score for the best concept suggestion for each term, we observe that examination terms extracted from the hospital information system perform better than those from questionnaires (\(p\)-value = 1.717 \(\times\) 10\({}^{-11}\), Welch Two Sample \(t\)-test), with mean similarity scores for examinations of 0.6717 (first quartile: 0.5486, median:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Comparison** & **Keywords** & **ORI** & **MIN** & **SYN** & **DUP** & **Similarity** & **USAGI Concept Name** \\ \hline \multirow{8}{*}{**OMOP**} & \multirow{8}{*}{**OMOP**} & \multirow{8}{*}{**OMOP**} & \multirow{8}{*}{**OMOP**} & \multirow{8}{*}{**OMOP**} & \(1\) & & \multicolumn{1}{c}{Lone atrial fibrillation} \\ \cline{4-7}  & & & & & & \(1\) & \multicolumn{1}{c}{Atrial fibrillation} \\ \cline{4-7}  & & & & & & \(0.89\) & \multicolumn{1}{c}{Atrial fibrillation detected} \\ \cline{4-7}  & & & & & & \(0.87\) & \multicolumn{1}{c}{H/O: atrial fibrillation} \\ \cline{4-7}  & & & & & & \(0.85\) & \multicolumn{1}{c}{Permanent atrial fibrillation} \\ \hline \multirow{8}{*}{**OMOP**} & \multirow{8}{*}{**OMOP**} & \multirow{8}{*}{**OMOP**} & \multirow{8}{*}{**OMOP**} & \(1\) & & \multicolumn{1}{c}{Myocardial infarction} \\ \cline{4-7}  & & & & & & \(0.81\) & \multicolumn{1}{c}{Dipnatic prediction} \\ \cline{4-7}  & & & & & \(0.78\) & \multicolumn{1}{c}{Dipnatic prediction} \\ \cline{4-7}  & & & & & \(0.63\) & \multicolumn{1}{c}{Dipnaostic procedure on heart} \\ \cline{4-7}  & & & & & \(0.61\) & \multicolumn{1}{c}{Atrial diagnosis} \\ \cline{4-7}  & & & & & \(0.64\) & \multicolumn{1}{c}{Arterial structure} \\ \cline{4-7}  & & & & & \(0.63\) & \multicolumn{1}{c}{Procedure to identify antibody} \\ \cline{4-7}  & & & & & \(0.6\) & \multicolumn{1}{c}{More0.6572, and third quartile: 0.7781), whereas for questionnaires of 0.6509 (first quartile: 0.5173, median: 0.6293, and third quartile: 0.7636).  
A total of 0.96% of examination variable names did not have any mapping suggestions (mean similarity score = 0), as did 0.75% of questionnaire variable names.  
In a random subset of variable names, the algorithm identified the same concept as one of its top five concept suggestions as an OMOF mapping expert in 56 percent of cases (50 of 90) (Supplementary Table S1, highlighted concepts).