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
VL - 294
SP - 357
EP - 361
JO - Studies in Health Technology and Informatics
JF - Studies in Health Technology and Informatics
SN - 0926-9630
ER -