DeIDNER corpus: Annotation of clinical discharge summary notes for named entity recognition using BRAT tool

Mahanazuddin Syed, Shaymaa Al-Shukri, Shorabuddin Syed, Kevin Sexton, Melody L. Greer, Meredith Zozus, Sudeepa Bhattacharyya, Fred Prior

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


Named Entity Recognition (NER) aims to identify and classify entities into predefined categories is a critical pre-processing task in Natural Language Processing (NLP) pipeline. Readily available off-the-shelf NER algorithms or programs are trained on a general corpus and often need to be retrained when applied on a different domain. The end model's performance depends on the quality of named entities generated by these NER models used in the NLP task. To improve NER model accuracy, researchers build domain-specific corpora for both model training and evaluation. However, in the clinical domain, there is a dearth of training data because of privacy reasons, forcing many studies to use NER models that are trained in the non-clinical domain to generate NER feature-set. Thus, influencing the performance of the downstream NLP tasks like information extraction and deidentification. In this paper, our objective is to create a high quality annotated clinical corpus for training NER models that can be easily generalizable and can be used in a downstream de-identification task to generate named entities feature-set.

Original languageEnglish (US)
Title of host publicationPublic Health and Informatics
Subtitle of host publicationProceedings of MIE 2021
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781643681856
ISBN (Print)9781643681849
StatePublished - Jul 1 2021


  • Annotation
  • Clinical Corpus
  • De-identification
  • Named Entity Recognition
  • Natural Language Processing

ASJC Scopus subject areas

  • Medicine(all)


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