# Semi-Automated Mapping of German Study Data Concepts to an English Common Data Model
###### Abstract  
The standardization of data from medical studies and hospital information systems to a common data model such as the Observational Medical Outcomes Partnership (OMOP) model can help make large datasets available for analysis using artificial intelligence approaches. Commonly, automatic mapping without intervention from domain experts delivers poor results. Further challenges arise from the need for translation of non-English medical data. Here, we report the establishment of a mapping approach which automatically translates German data variable names into English and suggests OMOP concepts. The approach was set up using study data from the Hamburg City Health Study. It was evaluated against the current standard, refined, and tested on a separate dataset. Furthermore, different types of graphical user interfaces for the selection of suggested OMOP concepts were created and assessed. Compared to the current standard our approach performs slightly better. Its main advantage lies in the automatic processing of German phrases into English OMOP concept suggestions, operating without the need for human intervention. Challenges still lie in the adequate translation of nonstandard expressions, as well as in the resolution of abbreviations into long names. OMOP; common data model; mapping; standardization; SNOMED CT; Germany; healthcare data; TF-IDF; HCHS +
Footnote †: journal: Medical Informatics, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; [EMAIL_ADDRESS] (R.T.); [EMAIL_ADDRESS] (S.B. context, whereas RxNorm (Medical Prescription Normalized) is the standard in the US American context. The ICD-10 (International Classification of Diseases) code offers another example. It is often used as a local version of the ICD-10 standard published by the WHO in a respective country. Thus, the ICD-10-FR used in France has semantic differences to the ICD-10-GM, which is used in Germany. The transmission of information between individual information systems can differ. Additionally, the different data standards can be structured with varying degrees of complexity. The ICD-10 standard uses a taxonomic structure, whereas SNOMED CT (SNOMED Clinical Terms)--a standard used, for example, in the USA for coding diagnoses and procedures--has an ontological structure. Taxonomic data standards form classes and, thus, structure the data hierarchically. Ontologies are built on concepts, which are related. Therefore, hierarchical but also non-hierarchical, i.e., logical, relations can be displayed [2]. Many individual data standards served their purpose in the past years, but, increasingly, the inclusion of different data standards within an HIS leads to digital processes being considerably slowed down. In recent years, various approaches have been explored to address these data management challenges regarding semantic interoperability. One approach that allows for a high degree of semantic interoperability is the use of a common data model (CDM). One well-known CDM is the Observational Medical Outcomes Partnership (OMOP) model of the Observational Health Data Sciences and Informatics (OHDSI) community. Local data are assigned to a standardized concept, which in turn is based on a data standard. The OMOP model is a relational data model. The clinical data area of the OMOP model stores patient data, whereas the standardized vocabularies build a large metadata repository within the OMOP model that includes several data standards such as ICD-10, SNOMED CT, or LOINC (Logical Observation Identifiers Names and Codes), a well-established standard for laboratory measurements. The vocabularies have to be actively included during the mapping process in order to standardize the local data and enable semantic interoperability. For example, the OMOP standard in the Condition_occurrence domain is SNOMED CT. Institutions that instead use ICD-10 for coding diagnoses can map their local ICD-10 codes to the respective SNOMED CT equivalent via the standardized vocabularies.