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

Hannes Ulrich; Hristina Uzunova; Heinz Handels; Josef Ingenerf

doi:10.3233/SHTI220474

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

Hannes Ulrich, Hristina Uzunova, Heinz Handels, Josef Ingenerf

Abstract

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.

Original language	English
Journal	Studies in Health Technology and Informatics
Volume	294
Pages (from-to)	357-361
Number of pages	5
ISSN	0926-9630
DOIs	https://doi.org/10.3233/SHTI220474
Publication status	Published - 25.05.2022

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SDG 3 Good Health and Well-being
SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)

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10.3233/SHTI220474

Cite this

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

Abstract

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