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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editores	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1548-1550
Número de páginas	3
ISBN (versión digital)	9781728118673
DOI	https://doi.org/10.1109/BIBM47256.2019.8983406
Estado	Publicada - nov. 2019
Publicado de forma externa	Sí
Evento	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, Estados Unidos Duración: 18 nov. 2019 → 21 nov. 2019

Serie de la publicación

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

Conferencia

Conferencia	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
País/Territorio	Estados Unidos
Ciudad	San Diego
Período	18/11/19 → 21/11/19

Nota bibliográfica

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.

ODS de las Naciones Unidas

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10.1109/BIBM47256.2019.8983406

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Lossio-Ventura, J. A., Boussard, S., Morzan, J., & Hernandez-Boussard, T. (2019). Clinical named-entity recognition: A short comparison. En I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1548-1550). Artículo 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.",

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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. En 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, Estados Unidos, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Boussard, Sebastien

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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. En Yoo I, Bi J, Hu XT, editores, 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

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