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Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH)

08.09. - 13.09.2024, Dresden

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Extending the TOP Framework with an Ontology-based Text Search Component

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  • Franz Matthies - Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany
  • Christoph Beger - Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany
  • Ralph Schäfermeier - Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany
  • Konrad Höffner - Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany
  • Alexandr Uciteli - Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany

Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH). Dresden, 08.-13.09.2024. Düsseldorf: German Medical Science GMS Publishing House; 2024. DocAbstr. 997

doi: 10.3205/24gmds115, urn:nbn:de:0183-24gmds1151

Published: September 6, 2024

© 2024 Matthies et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

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Introduction: Constructing search queries that deal with complex concepts is a challenging task without proficiency in the underlying query language – which holds true for either structured or unstructured data. Medical data might encompass both types, with valuable information present in one type but not the other.

Method: The TOP Framework provides clinical practitioners as well as researchers with a unified framework for querying diverse data types and, furthermore, facilitates an easier and intuitive approach. Additionally, it supports collaboration on query modeling and sharing.

Results: Having demonstrated its effectiveness with structured data, we introduce the integration of a component for unstructured data, specifically medical documents.

Conclusion: Our proof-of-concept shows a query language agnostic framework to model search queries for unstructured and structured data.

The authors declare that they have no competing interests.

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