Abstract
Ansprechperson
Beschreibung
The Terminology and Ontology-based Phenotyping (TOP) framework facilitates the collaborative creation and sharing of structured, explainable, and executable phenotype models. By utilising an intuitive web interface, the platform empowers clinical experts to develop models without the need for specialised IT expertise. This accessibility is combined with the integration of standardised terminologies such as SNOMED CT, which ensures that every model remains fully interoperable across diverse healthcare systems.
Beyond initial modelling, the framework provides the infrastructure for these definitions to be automatically executed across a wide range of data sources. This capability allows researchers to identify specific patient cohorts or perform semantic searches across medical databases and clinical documents.
FAQ
Phenotype models contain definitions for the following phenotype classes:
- Basic phenotype class: directly or indirectly observable traits of an organism, such as body height or diagnosis of diabetes mellitus type 2
- Derived phenotype class: combination of basic and other derived phenotype classes by applying functions, logical operators, or calculations
Each of them can be annotated with codes from standard terminologies.
The framework itself does not store any clinical data. However, data source-specific adapters can be used to connect the framework to a clinical data source, such as an FHIR server. Once this connection is established, queries can be executed on the data source. If enabled, the results containing the relevant clinical data for the phenotype model can then be downloaded.
Das Produkt im Einsatz
The TOP Framework can be installed locally or on your own server. Seamlessly move your work by exporting and importing phenotype models between instances.
A public instance of the TOP Framework is available at: https://top.imise.uni-leipzig.de/
Referenzen
Matthies F, Beger C, Schäfermeier R, Höffner K, Uciteli A. Extending the TOP Framework with an Ontology-Based Text Search Component. In: Röhrig R, Grabe N, Hübner UH, Jung K, Sax U, Schmidt CO, et al., editors. Studies in Health Technology and Informatics, IOS Press; 2024. https://doi.org/10.3233/SHTI240854.
Beger C, Boehmer AM, Mussawy B, Redeker L, Matthies F, Schäfermeier R, et al. Modelling Adverse Events with the TOP Phenotyping Framework. In: Röhrig R, Grabe N, Haag M, Hübner U, Sax U, Oliver Schmidt C, et al., editors. Studies in Health Technology and Informatics, IOS Press; 2023. https://doi.org/10.3233/SHTI230695.
Beger C, Matthies F, Schäfermeier R, Uciteli A. Model-driven execution of phenotype algorithms – introduction of the Terminology- and Ontology-based Phenotyping Framework. GMS Medizinische Informatik, Biometrie Und Epidemiologie 2023;19. https://doi.org/10.3205/MIBE000256.
Beger C, Matthies F, Schäfermeier R, Kirsten T, Herre H, Uciteli A. Towards an Ontology-Based Phenotypic Query Model. Applied Sciences. 2022 May 21;12(10):5214. https://doi.org/10.3390/app12105214.
Uciteli A, Beger C, Kirsten T, Meineke FA, Herre H. Ontological representation, classification and data-driven computing of phenotypes. J Biomed Semant. 2020 Dec;11(1):15. https://doi.org/10.1186/s13326-020-00230-0.