CDRnN: A high performance chemical-disease recognizer in biomedical literature

Hsin Chun Lee, Hung Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Diseases/Chemical play central roles in many areas of biomedical research and healthcare. Consequently, aggregating the disease knowledge and treatment research reports becomes an extremely critical issue, especially in rapid-growth knowledge bases (e.g., PubMed). Thus, a framework of disease/chemical named entity recognition and normalization has become increasingly important for biomedical text mining. In this work, we not only define five diversities of disease names but also develop a system for disease/chemical mention recognition and normalization in biomedical texts. Our system utilizes an order 2 conditional random fields (CRFs) model to develop a recognition system and optimize the results by customizing several post-processing, including abbreviation resolution, consistency improvement, stopwords filtering, and adjectives reorganization. After evaluation, we obtained the best performance (86.9% of F-score) on disease normalization and (89.95% of Precision) on chemical normalization. These results suggest that our system is a high-performance and state of the art recognition system for disease/chemical recognition and normalization from biomedical literature.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-379
Number of pages6
ISBN (Electronic)9781509030491
DOIs
Publication statusPublished - 2017 Dec 15
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: 2017 Nov 132017 Nov 16

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period17-11-1317-11-16

Fingerprint

Data Mining
Knowledge Bases
PubMed
Names
Biomedical Research
Delivery of Health Care
Growth
Processing

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

Cite this

Lee, H. C., & Kao, H. Y. (2017). CDRnN: A high performance chemical-disease recognizer in biomedical literature. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C-R. Shyu, Y. Bromberg, J. Gao, ... D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 374-379). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217678
Lee, Hsin Chun ; Kao, Hung Yu. / CDRnN : A high performance chemical-disease recognizer in biomedical literature. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 374-379 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).
@inproceedings{ed5cc60d53554e4590dd26490454e4e5,
title = "CDRnN: A high performance chemical-disease recognizer in biomedical literature",
abstract = "Diseases/Chemical play central roles in many areas of biomedical research and healthcare. Consequently, aggregating the disease knowledge and treatment research reports becomes an extremely critical issue, especially in rapid-growth knowledge bases (e.g., PubMed). Thus, a framework of disease/chemical named entity recognition and normalization has become increasingly important for biomedical text mining. In this work, we not only define five diversities of disease names but also develop a system for disease/chemical mention recognition and normalization in biomedical texts. Our system utilizes an order 2 conditional random fields (CRFs) model to develop a recognition system and optimize the results by customizing several post-processing, including abbreviation resolution, consistency improvement, stopwords filtering, and adjectives reorganization. After evaluation, we obtained the best performance (86.9{\%} of F-score) on disease normalization and (89.95{\%} of Precision) on chemical normalization. These results suggest that our system is a high-performance and state of the art recognition system for disease/chemical recognition and normalization from biomedical literature.",
author = "Lee, {Hsin Chun} and Kao, {Hung Yu}",
year = "2017",
month = "12",
day = "15",
doi = "10.1109/BIBM.2017.8217678",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "374--379",
editor = "Illhoi Yoo and Zheng, {Jane Huiru} and Yang Gong and Hu, {Xiaohua Tony} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",
booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
address = "United States",

}

Lee, HC & Kao, HY 2017, CDRnN: A high performance chemical-disease recognizer in biomedical literature. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 374-379, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 17-11-13. https://doi.org/10.1109/BIBM.2017.8217678

CDRnN : A high performance chemical-disease recognizer in biomedical literature. / Lee, Hsin Chun; Kao, Hung Yu.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 374-379 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - CDRnN

T2 - A high performance chemical-disease recognizer in biomedical literature

AU - Lee, Hsin Chun

AU - Kao, Hung Yu

PY - 2017/12/15

Y1 - 2017/12/15

N2 - Diseases/Chemical play central roles in many areas of biomedical research and healthcare. Consequently, aggregating the disease knowledge and treatment research reports becomes an extremely critical issue, especially in rapid-growth knowledge bases (e.g., PubMed). Thus, a framework of disease/chemical named entity recognition and normalization has become increasingly important for biomedical text mining. In this work, we not only define five diversities of disease names but also develop a system for disease/chemical mention recognition and normalization in biomedical texts. Our system utilizes an order 2 conditional random fields (CRFs) model to develop a recognition system and optimize the results by customizing several post-processing, including abbreviation resolution, consistency improvement, stopwords filtering, and adjectives reorganization. After evaluation, we obtained the best performance (86.9% of F-score) on disease normalization and (89.95% of Precision) on chemical normalization. These results suggest that our system is a high-performance and state of the art recognition system for disease/chemical recognition and normalization from biomedical literature.

AB - Diseases/Chemical play central roles in many areas of biomedical research and healthcare. Consequently, aggregating the disease knowledge and treatment research reports becomes an extremely critical issue, especially in rapid-growth knowledge bases (e.g., PubMed). Thus, a framework of disease/chemical named entity recognition and normalization has become increasingly important for biomedical text mining. In this work, we not only define five diversities of disease names but also develop a system for disease/chemical mention recognition and normalization in biomedical texts. Our system utilizes an order 2 conditional random fields (CRFs) model to develop a recognition system and optimize the results by customizing several post-processing, including abbreviation resolution, consistency improvement, stopwords filtering, and adjectives reorganization. After evaluation, we obtained the best performance (86.9% of F-score) on disease normalization and (89.95% of Precision) on chemical normalization. These results suggest that our system is a high-performance and state of the art recognition system for disease/chemical recognition and normalization from biomedical literature.

UR - http://www.scopus.com/inward/record.url?scp=85046249476&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046249476&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2017.8217678

DO - 10.1109/BIBM.2017.8217678

M3 - Conference contribution

AN - SCOPUS:85046249476

T3 - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

SP - 374

EP - 379

BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

A2 - Yoo, Illhoi

A2 - Zheng, Jane Huiru

A2 - Gong, Yang

A2 - Hu, Xiaohua Tony

A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

A2 - Gao, Jean

A2 - Korkin, Dmitry

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Lee HC, Kao HY. CDRnN: A high performance chemical-disease recognizer in biomedical literature. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu C-R, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 374-379. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). https://doi.org/10.1109/BIBM.2017.8217678