Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations

Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). The MDM Portal is a registered European information in...

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Bibliographic Details
Published in:Methods of information in medicine Vol. 63; no. 1-02; p. 52
Main Authors: Riepenhausen, Sarah, Blumenstock, Max, Niklas, Christian, Hegselmann, Stefan, Neuhaus, Philipp, Meidt, Alexandra, Püttmann, Cornelia, Storck, Michael, Ganzinger, Matthias, Varghese, Julian, Dugas, Martin
Format: Journal Article
Language:English
Published: Germany 01.05.2024
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ISSN:2511-705X, 2511-705X
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Summary:Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. The most frequent keyword is "clinical trial" (  = 18,777), and the most frequent disease-specific keyword is "breast neoplasms" (  = 1,943). Most data items are available in English (  = 545,749) and German (  = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing.
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ISSN:2511-705X
2511-705X
DOI:10.1055/s-0044-1786839