BioCreative/OHNLP Challenge 2018

Majid Rastegar-Mojarad, Sijia Liu, Yanshan Wang, Naveed Afzal, Liwei Wang, Feichen Shen, Sunyang Fu, Hongfang Liu

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

6 Scopus citations

Abstract

The application of Natural Language Processing (NLP) methods and resources to clinical and biomedical text has received growing attention over the past years, but progress has been limited by difficulties to access shared tools and resources, partially caused by patient privacy and data confidentiality constraints. Efforts to increase sharing and interoperability of the few existing resources are needed to facilitate the progress observed in the general NLP domain. Leveraging our research in corpus analysis and de-identification research, we have created multiple synthetic data sets for a couple of NLP tasks based on real clinical sentences. We are organizing a challenge workshop to promote community efforts towards the advancement in clinical NLP. The challenge workshop will have two tasks: 1) Family History Information Extraction; and 2) Clinical Semantic Textual Similarity.

Original languageEnglish (US)
Title of host publicationACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages575
Number of pages1
ISBN (Electronic)9781450357944
DOIs
StatePublished - Aug 15 2018
Event9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018 - Washington, United States
Duration: Aug 29 2018Sep 1 2018

Publication series

NameACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018
Country/TerritoryUnited States
CityWashington
Period8/29/189/1/18

Keywords

  • Information extraction
  • Natural language processing
  • Semantic textual similarity

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics
  • Biomedical Engineering

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