Clinical Name Entity Recognition Based on Recurrent Neural Networks

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

doi:10.1109/ICCSA.2018.8439147

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 Book › Conference contribution › peer-review

10 Citations (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 language	English
Title of host publication	Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018
Editors	David Taniar, Eufemia Tarantino, Beniamino Murgante Sanjay Misra, Osvaldo Gervasi, Carmelo M. Torre, Yeonseung Ryu, Elena Stankova, Ana Maria A.C. Rocha, Bernady O. Apduhan
Place of Publication	Australia
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-9
Number of pages	9
ISBN (Electronic)	9781538672143
ISBN (Print)	9781538672150
DOIs	https://doi.org/10.1109/ICCSA.2018.8439147
Publication status	Published - 2 Jul 2018
Event	18th International Conference on Computational Science and Its Applications - Melbourne, Australia Duration: 2 Jul 2018 → 5 Jul 2018

Conference

Conference	18th International Conference on Computational Science and Its Applications
Abbreviated title	ICCSA 2018
Country/Territory	Australia
City	Melbourne
Period	2/07/18 → 5/07/18

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10.1109/ICCSA.2018.8439147

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LUU, T. M., PHAN, R., DAVEY, R., & CHETTY, G. (2018). Clinical Name Entity Recognition Based on Recurrent Neural Networks. In D. Taniar, E. Tarantino, B. M. S. Misra, O. Gervasi, C. M. Torre, Y. Ryu, E. Stankova, A. M. A. C. Rocha, & B. O. Apduhan (Eds.), Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018 (pp. 1-9). Article 8439147 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCSA.2018.8439147

LUU, Thoai Man ; PHAN, Rob ; DAVEY, Rachel et al. / Clinical Name Entity Recognition Based on Recurrent Neural Networks. Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018. editor / David Taniar ; Eufemia Tarantino ; Beniamino Murgante Sanjay Misra ; Osvaldo Gervasi ; Carmelo M. Torre ; Yeonseung Ryu ; Elena Stankova ; Ana Maria A.C. Rocha ; Bernady O. Apduhan. Australia : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 1-9

@inproceedings{613de18834864da68dd5fa38a53a6d15,

title = "Clinical Name Entity Recognition Based on Recurrent Neural Networks",

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.",

keywords = "medical computing, machine learning, Girija, Chetty",

author = "LUU, {Thoai Man} and Rob PHAN and Rachel DAVEY and Girija CHETTY",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/ICCSA.2018.8439147",

language = "English",

isbn = "9781538672150",

pages = "1--9",

editor = "David Taniar and Eufemia Tarantino and Misra, {Beniamino Murgante Sanjay} and Osvaldo Gervasi and Torre, {Carmelo M.} and Yeonseung Ryu and Elena Stankova and Rocha, {Ana Maria A.C.} and Apduhan, {Bernady O.}",

booktitle = "Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "18th International Conference on Computational Science and Its Applications, ICCSA 2018 ; Conference date: 02-07-2018 Through 05-07-2018",

}

LUU, TM, PHAN, R, DAVEY, R & CHETTY, G 2018, Clinical Name Entity Recognition Based on Recurrent Neural Networks. in D Taniar, E Tarantino, BMS Misra, O Gervasi, CM Torre, Y Ryu, E Stankova, AMAC Rocha & BO Apduhan (eds), Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018., 8439147, IEEE, Institute of Electrical and Electronics Engineers, Australia, pp. 1-9, 18th International Conference on Computational Science and Its Applications, Melbourne, Australia, 2/07/18. https://doi.org/10.1109/ICCSA.2018.8439147

Clinical Name Entity Recognition Based on Recurrent Neural Networks. / LUU, Thoai Man; PHAN, Rob; DAVEY, Rachel et al.
Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018. ed. / David Taniar; Eufemia Tarantino; Beniamino Murgante Sanjay Misra; Osvaldo Gervasi; Carmelo M. Torre; Yeonseung Ryu; Elena Stankova; Ana Maria A.C. Rocha; Bernady O. Apduhan. Australia: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 1-9 8439147.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Clinical Name Entity Recognition Based on Recurrent Neural Networks

AU - LUU, Thoai Man

AU - PHAN, Rob

AU - DAVEY, Rachel

AU - CHETTY, Girija

PY - 2018/7/2

Y1 - 2018/7/2

N2 - 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.

AB - 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.

KW - medical computing

KW - machine learning

KW - Girija

KW - Chetty

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DO - 10.1109/ICCSA.2018.8439147

M3 - Conference contribution

SN - 9781538672150

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EP - 9

BT - Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018

A2 - Taniar, David

A2 - Tarantino, Eufemia

A2 - Misra, Beniamino Murgante Sanjay

A2 - Gervasi, Osvaldo

A2 - Torre, Carmelo M.

A2 - Ryu, Yeonseung

A2 - Stankova, Elena

A2 - Rocha, Ana Maria A.C.

A2 - Apduhan, Bernady O.

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Australia

T2 - 18th International Conference on Computational Science and Its Applications

Y2 - 2 July 2018 through 5 July 2018

ER -

LUU TM, PHAN R, DAVEY R , CHETTY G. Clinical Name Entity Recognition Based on Recurrent Neural Networks. In Taniar D, Tarantino E, Misra BMS, Gervasi O, Torre CM, Ryu Y, Stankova E, Rocha AMAC, Apduhan BO, editors, Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018. Australia: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 1-9. 8439147 doi: 10.1109/ICCSA.2018.8439147

Clinical Name Entity Recognition Based on Recurrent Neural Networks

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