Clinical Name Entity Recognition Based on Recurrent Neural Networks

Thoai Man LUU, Rob PHAN, Rachel DAVEY, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a novel approach for clinical name entity recognition based on deep machine learning architecture. The proposed scheme based on two different deep learning architectures: the feed forward networks (FFN), and the recurrent neural network (RNN), allow significant improvement in performance, in terms of different performance measures, including precision, recall and F-score, when evaluated with the CLEF 2016 Challenge task 1 A dataset corresponding to Clinical Nursing Handover. It was possible to achieve an F-score of 66% with RNN architecture, which was higher than most of the other participating systems in the Challenge task.
Original languageEnglish
Title of host publicationProceedings 2018 18th International Conference on Computational Science and Applications (ICCSA)
Place of PublicationMelbourne, VIC, Australia
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-9
Number of pages9
ISBN (Electronic)9781538672143
ISBN (Print)9781538672150
DOIs
Publication statusPublished - 2 Jul 2018
Event18th International Conference on Computational Science and Its Applications - Melbourne, Australia
Duration: 2 Jul 20185 Jul 2018

Conference

Conference18th International Conference on Computational Science and Its Applications
Abbreviated titleICCSA 2018
CountryAustralia
CityMelbourne
Period2/07/185/07/18

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  • Cite this

    LUU, T. M., PHAN, R., DAVEY, R., & CHETTY, G. (2018). Clinical Name Entity Recognition Based on Recurrent Neural Networks. In Proceedings 2018 18th International Conference on Computational Science and Applications (ICCSA) (pp. 1-9). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCSA.2018.8439147