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

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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