¹Universität Leipzig, Leipzig, Germany
²Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
³Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig, Germany
⁴Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany

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Introduction: Sharing clinical research data is vital for medical progress, yet challenges persist due to complex formats, restricted access, and insufficient exploration tools. Next-generation phenotyping tackles these issues by integrating semantic, standardised phenotype definitions with executable algorithms [1]. This approach enables efficient disease detection, participant selection, and computation of complex phenotypes like risk scores. The Terminology- and Ontology-based Phenotyping (TOP) Framework embodies this methodology.

Methods: To standardise phenotype models, we developed the Core Ontology of Phenotypes (COP) [2], formally defining key concepts such as 'phenotype', 'phenotype class', and 'phenotype restriction'. Building on COP, we introduced the TOP API, an OpenAPI specification outlining content types (e.g., phenotype definitions, repositories) and business logic for a phenotype repository. This framework facilitates the development, management, and sharing of standardised phenotype models and supports phenotypic queries across health data stores.

Results: The TOP Framework, grounded in COP and the TOP API, enables the creation of computable phenotype models using standard terminologies. It offers a standardised query interface for diverse data sources, enhancing data exploration and analysis. Features include access management and the ability to share and reuse models. The framework has been successfully applied in real-world settings, such as specifying adverse event algorithms in the INTERPOLAR project and improving data sharing within the LIFE study, demonstrating its potential to enhance clinical research efficiency and collaboration.? Latest developments include a novel approach for the formal representation of unstructured medical documents [3], ontology-based search [4], and automated change trackinig in medical terminologies [5].

Conclusion: Addressing the challenges in standardising phenotype models, the TOP Framework – built upon COP and the TOP API – empowers researchers with structured models and efficient data exploration tools. Its collaborative features align with the goals of the Medical Informatics Initiative (MII) and facilitate integration into its infrastructure, streamlining data access. Real-world applications underscore the framework's capacity to transform clinical research through improved efficiency, collaboration, and knowledge discovery.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.

Literatur

[1] Mo H, Thompson WK, Rasmussen LV, Pacheco JA, Jiang G, Kiefer R, et al. Desiderata for computable representations of electronic health records-driven phenotype algorithms. Journal of the American Medical Informatics Association. 2015 Nov 1;22(6):1220–30.
[2] Uciteli A, Beger C, Kirsten T, Meineke FA, Herre H. Ontological Representation, Classification and Data-Driven Computing of Phenotypes. Journal of Biomedical Semantics. 2020 Dec 21;11(1):15.
[3] Matthies F, Beger C, Schäfermeier R, Uciteli A. Concept Graphs: A Novel Approach for Textual Analysis of Medical Documents. In: German Medical Data Sciences 2023 – Science Close to People. IOS Press; 2023. p. 172–9. DOI: 10.3233/SHTI230710
[4] Matthies F, Beger C, Schäfermeier R, Höffner K, Uciteli A. Extending the TOP Framework with an Ontology-Based Text Search Component. Stud Health Technol Inform. 2024;317:180–9.
[5] Schäfermeier R, Beger C, Matthies F, Hoeffner K, Uciteli A. Tracking Changes for Inter-Version Interoperability in Heterogeneous Evolving Medical Terminologies. In: Röhrig R, Grabe N, Hübner UH, Jung K, Sax U, Schmidt CO, et al, editors. German Medical Data Sciences 2024. Health – Thinking, Researching and Acting Together. Proceedings of the 69th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2024 in Dresden, Germany. IOS Press; 2024. (Studies in Health Technology and Informatics; 317). DOI: 10.3233/SHTI240855