25gmds108 10.3205/25gmds108 urn:nbn:de:0183-25gmds1086 Meeting Abstract Hofenbitzer Hofenbitzer Justin J

Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital, Munich, Germany

author Schulz Schulz Stefan S

Medizinische Universität Graz, Graz, Austria Averbis GmbH, Freiburg, Germany

author Boeker Boeker Martin M

Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital, Munich, Germany

author Klügl Klügl Peter P

Averbis GmbH, Freiburg, Germany

author Riepenhausen Riepenhausen Sarah S

University of Münster, Institute of Medical Informatics, Münster, Germany

author Lohr Lohr Christina C

Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany

author Lammert Lammert Jacqueline J

Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital, Munich, Germany

author Riedel Riedel Andrea A

Erlangen University Hospital, Medical Center for Information and Communication Technology, Erlangen, Germany Friedrich-Alexander-Universität Erlangen-Nürnberg, Medical Informatics, Erlangen, Germany

author Modersohn Modersohn Luise L

Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital, Munich, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 semantics natural language processing linguistics systematized nomenclature of medicine corpus annotation German clinical text 20251103 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 108 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) PS 2: Interoperability and standards Jena 20250907 20250911 Abstr. 282 TextIntroduction: The systematic annotation of clinical free-text documents with standardized terminologies, such as SNOMED CT (SCT), is essential for developing interoperable and semantically enriched healthcare resources. Domain-specific natural language corpora with meaningful and expressive annotations are fundamental for computational linguistics and natural language processing (NLP), enabling tasks like training, fine-tuning, and evaluating large language models. However, there is a shortage of clinical corpora , and only a few contain semantic annotations . Prominent counterexamples are projects like AIDAVA (https://www.aidava.eu) or JIGSAW (https://research.manchester.ac.uk/en/projects/assembling-the-data-jigsaw-powering-robust-research-on-the-causes), which paved the way towards standardized semantic text annotations using existing clinical ontologies . The German Medical Text Corpus (GeMTeX) project aims to provide the largest shareable German clinical document collection with conceptual annotations from SCT , , and hereby introduces its detailed and methodologically grounded annotation guidelines.Methods: The guidelines are informed by experiences and annotation principles from comparable consortia . These experiences emphasize focusing on the semantic core of the texts, specifically by utilizing unary predicates, the SCT concepts, and binary relations between them. The annotations should be as literal and straightforward as possible to prevent the annotators from over-interpreting the text spans. Results: The GeMTeX semantic annotation guidelines define three major concept classes: Core Concepts, Modifier Concepts, and Qualifier Concepts. The Core Concepts are the most relevant concepts for our semantic annotation, as they include Clinical Conditions (e.g., the SCT hierarchies Clinical Finding and Disorder), Procedures, Medications, Substances, and Observables. The Modifier and Qualifier Concepts specify specific annotations if a Core Concept does not sufficiently express the corresponding text span. The Modifier Concepts covered by our guidelines comprise the SCT hierarchies Body Structure, Physical Object, and Organism. The Qualifier Concept is identical to the SCT hierarchy qualifier value and contains, among others, dates, units, or factuality statements. Familial diseases are featured as well.To build traceable knowledge representations, we introduce relations between annotated concepts. The unlabeled and unidirectional relations follow intuitive and concise rules. For example, relations always point from a cause to its corresponding effect or go from the lower to the upper bound within a value range. In addition, our guidelines are specifically tailored to the German language. Besides universal linguistic challenges like ambiguity, syntactic coordination, or copula constructions, we provide concise and comprehensible instructions for typical German peculiarities, e.g., separable particle verbs or unique terminology.Conclusion: The GeMTeX semantic annotation guidelines are informed by large semantic annotation projects for free-text documents in the clinical domain. They specifically address the usage of SCT and underline linguistic and terminological challenges inherent to the German clinical language. Explicit annotation rules and decision-making criteria accompanied by illustrative examples are integral to enhancing consistency between annotators. This initial version of the annotation guidelines is designed to be a methodological resource for researchers in clinical NLP or terminology annotation, supporting reproducibility and transparency. We aim to contribute to further standardization efforts. The GeMTeX annotation guidelines are available under https://doi.org/10.5281/zenodo.15689931.The authors declare that they have no competing interests.The authors declare that a positive ethics committee vote has been obtained. Hahn U Clinical Document Corpora -- Real Ones, Translated and Synthetic Substitutes, and Assorted Domain Proxies: A Survey of Diversity in Corpus Design, with Focus on German Text Data 2025 Jamia Open ooaf024 Hahn U. Clinical Document Corpora -- Real Ones, Translated and Synthetic Substitutes, and Assorted Domain Proxies: A Survey of Diversity in Corpus Design, with Focus on German Text Data. Jamia Open. 2025;8(3):ooaf024. DOI: 10.1093/jamiaopen/ooaf024 http://dx.doi.org/10.1093/jamiaopen/ooaf024 Jovanović J Bagheri E Semantic Annotation in Biomedicine: The Current Landscape 2017 J Biomed Semant 44 Jovanović J, Bagheri E. Semantic Annotation in Biomedicine: The Current Landscape. J Biomed Semant. 2017 Sep;8(1):44. Schulz S Del-Pinto W Han L Kreuzthaler M Aghaei S Nenadic G Towards Principles of Ontology-Based Annotation of Clinical Narratives 4.0 International (CC BY 4.0) Proceedings of the International Conference on Biomedical Ontologies 2023; 2023 Aug 28 - Sep 1; Brasilia, Brazil Schulz S, Del-Pinto W, Han L, Kreuzthaler M, Aghaei S, Nenadic G. Towards Principles of Ontology-Based Annotation of Clinical Narratives 40 International (CC BY 40). In: Proceedings of the International Conference on Biomedical Ontologies 2023; 2023 Aug 28 - Sep 1; Brasilia, Brazil. (CEUR Workshop Proceedings; 3603). [cited 2025 Apr 25]. Available from: https://ceur-ws.org/Vol-3603/Paper4.pdf https://ceur-ws.org/Vol-3603/Paper4.pdf Meineke F Modersohn L Loeffler M Boeker M Announcement of the German Medical Text Corpus Project (GeMTeX) 2023 Caring is Sharing – Exploiting the Value in Data for Health and Innovation. Proceedings of MIE 2023 Meineke F, Modersohn L, Loeffler M, Boeker M. Announcement of the German Medical Text Corpus Project (GeMTeX). In: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. Proceedings of MIE 2023. IOS; 2023. DOI: 10.3233/SHTI230283 https://doi.org/10.3233/SHTI230283 Lohr C Matthies F Faller J Modersohn L Riedel A Hahn U Kiser R Boeker M Meineke F De-Identifying GRASCCO - A Pilot Study for the De-Identification of the German Medical Text Project (GeMTeX) Corpus 2024 Stud Health Technol Inform 171-179 Lohr C, Matthies F, Faller J, Modersohn L, Riedel A, Hahn U, Kiser R, Boeker M, Meineke F. De-Identifying GRASCCO - A Pilot Study for the De-Identification of the German Medical Text Project (GeMTeX) Corpus. Stud Health Technol Inform. 2024 Aug 30;317:171-179. DOI: 10.3233/SHTI240853 https://doi.org/10.3233/SHTI240853 0 0 0 0