Proposal of Semantic Annotation for German Metadata Using Bidirectional Recurrent Neural Networks

Hannes Ulrich; Hristina Uzunova; Heinz Handels; Josef Ingenerf

In: Studies in Health Technology and Informatics, Vol. 294, Pages 357-361, IOS Press, 2022.


The distributed nature of our digital healthcare and the rapid emergence of new data sources prevents a compelling overview and the joint use of new data. Data integration, e.g., with metadata and semantic annotations, is expected to overcome this challenge. In this paper, we present an approach to predict UMLS codes to given German metadata using recurrent neural networks. The augmentation of the training dataset using the Medical Subject Headings (MeSH), particularly the German translations, also improved the model accuracy. The model demonstrates robust performance with 75% accuracy and aims to show that increasingly sophisticated machine learning tools can already play a significant role in data integration.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence