# Semi-Automated Mapping of German Study Data Concepts to an English Common Data Model
###### Abstract
This approach generates a high degree of semantic interoperability. The standardization of medical data harbors great potential as it provides the basis to create large medical datasets through either data pooling or federated learning for analysis using notoriously data-hungry artificial intelligence methods [3]. Efforts are already underway to map German medical data to the OMOP CDM by applying the new Episode Domain to German Cancer Registry data [4], implementing OMOP at eight German hospitals [5], using HL7 FHIR to integrate German registry data into OMOP [6], mapping German infection-control-related data across openEHR, FHIR, and OMOP [7], creating a concept to transfer German drug [8] and procedure data [9] to OMOP, as well as by the establishment of an ETL (Extraction, Transformation, Loading)-process to OMOP for all German university hospitals [10]. However, challenges arise for one from medical data that does not adhere to any established data standard. This is frequently the case for study data, which is often tailored towards a specific research goal, and especially within questionnaire data. Furthermore, problems occur from the need for translation of non-English to English, which becomes necessary if specific vocabularies are not available in a certain language. OHDSI provides a set of software tools to help prepare ETLs of structured data from common terminologies, vocabularies, and coding schemes called White Rabbit and Rabbit-In-A-Hat. To help map source codes, preferably from standard terminologies, to OMOP the OHDSI program offers USAGI [11]. It has, for example, recently been used to map clinical studies by condition to OMOP [12]. Usagi performs similarity mapping using term frequency-inverse document frequency (TF-IDF). TF-IDF is a statistical measure that represents term importance. It is a popular method often used by search engines. However, TF-IDF similarities are based merely on similar occurrences of keywords. Model training as in machine learning applications is not possible. Also, USAGI does not provide an option to translate non-English codes to English but suggests using Google Translate. Recently, a deep-learning-based approach to terminology mapping to OMOP called TOKI has been published [13]. In contrast to TF-IDF, TOKI uses embedding-based semantic similarities where words are embedded into a semantic space defined primarily through their co-occurrence within text corpora. This makes individual word vectors easily comparable. TOKI reports a greater than ten percent improved matching accuracy compared to USAGI. Unfortunately, it does not provide a translation function and the source code of TOKI is not publicly available. Furthermore, an NLP-based software solution called CLAMP exists [14]. It comprises a graphical user interface (GUI) to build customized NLP pipelines of sequential NLP tasks including tokenization, sentence boundary detection, part-of-speech tagger, named entity recognition, and others. It approaches clinical concept extraction as a supervised named entity recognition (NER) task and has recently been used to map COVID-19 signs and symptoms from clinical text to OMOP concepts [15]. NLP-based methods for the mapping of clinical text to OMOP such as CLAMP and related are being promoted by the OHDSI Natural Language Processing Working Group [16]. In this study, we use the TF-IDF method similarly to USAGI to map medical data from the Hamburg City Health Study (HCHS) to OMOP concepts. The HCHS is a large, population-based cohort study of 45,000 participants from the general population of Hamburg, Germany [17]. Participants undergo 18 examinations primarily targeting major organ system functions and structures including extensive imaging examinations. Additionally, before, during, and after the baseline visit validated self-report questionnaires asking for a variety of lifestyle and environmental conditions and habits are filled out.