Clinical named-entity recognition: A short comparison

Juan Antonio Lossio-Ventura; Sebastien Boussard; Juandiego Morzan; Tina Hernandez-Boussard

doi:10.1109/BIBM47256.2019.8983406

Clinical named-entity recognition: A short comparison

Juan Antonio Lossio-Ventura, Sebastien Boussard, Juandiego Morzan, Tina Hernandez-Boussard

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The adoption of electronic health records has increased the volume of clinical data, which has opened an opportunity for healthcare research. There are several biomedical annotation systems that have been used to facilitate the analysis of clinical data. However, there is a lack of clinical annotation comparisons to select the most suitable tool for a specific clinical task. In this work, we used clinical notes from the MIMIC-III database and evaluated three annotation systems to identify four types of entities: (1) procedure, (2) disorder, (3) drug, and (4) anatomy. Our preliminary results demonstrate that BioPortal performs well when extracting disorder and drug. This can provide clinical researchers with real-clinical insights into patient's health patterns and it may allow to create a first version of an annotated dataset.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1548-1550
Number of pages	3
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983406
State	Published - Nov 2019
Externally published	Yes
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

clinical research
electronic health records
named-entity recognition
natural language processing

UN SDGs

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

Access to Document

10.1109/BIBM47256.2019.8983406

Cite this

Lossio-Ventura, J. A., Boussard, S., Morzan, J., & Hernandez-Boussard, T. (2019). Clinical named-entity recognition: A short comparison. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1548-1550). Article 8983406 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983406

Lossio-Ventura, Juan Antonio ; Boussard, Sebastien ; Morzan, Juandiego et al. / Clinical named-entity recognition : A short comparison. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1548-1550 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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abstract = "The adoption of electronic health records has increased the volume of clinical data, which has opened an opportunity for healthcare research. There are several biomedical annotation systems that have been used to facilitate the analysis of clinical data. However, there is a lack of clinical annotation comparisons to select the most suitable tool for a specific clinical task. In this work, we used clinical notes from the MIMIC-III database and evaluated three annotation systems to identify four types of entities: (1) procedure, (2) disorder, (3) drug, and (4) anatomy. Our preliminary results demonstrate that BioPortal performs well when extracting disorder and drug. This can provide clinical researchers with real-clinical insights into patient's health patterns and it may allow to create a first version of an annotated dataset.",

keywords = "clinical research, electronic health records, named-entity recognition, natural language processing",

author = "Lossio-Ventura, {Juan Antonio} and Sebastien Boussard and Juandiego Morzan and Tina Hernandez-Boussard",

note = "Funding Information: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA183962. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

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Lossio-Ventura, JA, Boussard, S, Morzan, J & Hernandez-Boussard, T 2019, Clinical named-entity recognition: A short comparison. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983406, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1548-1550, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983406

Clinical named-entity recognition: A short comparison. / Lossio-Ventura, Juan Antonio; Boussard, Sebastien; Morzan, Juandiego et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1548-1550 8983406 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lossio-Ventura, Juan Antonio

AU - Boussard, Sebastien

AU - Morzan, Juandiego

AU - Hernandez-Boussard, Tina

N1 - Funding Information: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA183962. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: © 2019 IEEE.

PY - 2019/11

Y1 - 2019/11

N2 - The adoption of electronic health records has increased the volume of clinical data, which has opened an opportunity for healthcare research. There are several biomedical annotation systems that have been used to facilitate the analysis of clinical data. However, there is a lack of clinical annotation comparisons to select the most suitable tool for a specific clinical task. In this work, we used clinical notes from the MIMIC-III database and evaluated three annotation systems to identify four types of entities: (1) procedure, (2) disorder, (3) drug, and (4) anatomy. Our preliminary results demonstrate that BioPortal performs well when extracting disorder and drug. This can provide clinical researchers with real-clinical insights into patient's health patterns and it may allow to create a first version of an annotated dataset.

AB - The adoption of electronic health records has increased the volume of clinical data, which has opened an opportunity for healthcare research. There are several biomedical annotation systems that have been used to facilitate the analysis of clinical data. However, there is a lack of clinical annotation comparisons to select the most suitable tool for a specific clinical task. In this work, we used clinical notes from the MIMIC-III database and evaluated three annotation systems to identify four types of entities: (1) procedure, (2) disorder, (3) drug, and (4) anatomy. Our preliminary results demonstrate that BioPortal performs well when extracting disorder and drug. This can provide clinical researchers with real-clinical insights into patient's health patterns and it may allow to create a first version of an annotated dataset.

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ER -

Lossio-Ventura JA, Boussard S, Morzan J, Hernandez-Boussard T. Clinical named-entity recognition: A short comparison. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1548-1550. 8983406. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983406

Clinical named-entity recognition: A short comparison

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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