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

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/SHTI220474

Cite this

@article{e241896fc9b04a1ebc5dfdddc8ab24a7,

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

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.",

author = "Hannes Ulrich and Hristina Uzunova and Heinz Handels and Josef Ingenerf",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2022",

month = may,

day = "25",

doi = "10.3233/SHTI220474",

language = "English",

volume = "294",

pages = "357--361",

journal = "Studies in Health Technology and Informatics",

issn = "0926-9630",

publisher = "IOS Press BV",

}

TY - JOUR

T1 - Proposal of Semantic Annotation for German Metadata Using Bidirectional Recurrent Neural Networks

AU - Ulrich, Hannes

AU - Uzunova, Hristina

AU - Handels, Heinz

AU - Ingenerf, Josef

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2022/5/25

Y1 - 2022/5/25

N2 - 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.

AB - 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.

UR - https://www.mendeley.com/catalogue/5b994404-1f46-3de0-a24a-ed4f99dddd44/

U2 - 10.3233/SHTI220474

DO - 10.3233/SHTI220474

M3 - Journal articles

C2 - 35612096

SN - 0926-9630

VL - 294

SP - 357

EP - 361

JO - Studies in Health Technology and Informatics

JF - Studies in Health Technology and Informatics

ER -

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

Abstract

Research Areas and Centers

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this