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 proceedingConference contributionpeer-review

1 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 languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1548-1550
Number of pages3
ISBN (Electronic)9781728118673
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: 18 Nov 201921 Nov 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/TerritoryUnited States
CitySan Diego
Period18/11/1921/11/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

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

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