Document Type
Article
Publication Title
Studies in health technology and informatics
PubMed ID
34042780
MeSH Headings (Medical Subject Headings)
Algorithms; Humans; Information Storage and Retrieval; Names; Natural Language Processing; Patient Discharge
Abstract
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 de-identification. 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.
First Page
432
Last Page
436
DOI
10.3233/SHTI210195
Publication Date
5-27-2021
Recommended Citation
Syed, Mahanazuddin; Al-Shukri, Shaymaa; Syed, Shorabuddin; Sexton, Kevin; Greer, Melody L.; Zozus, Meredith; Bhattacharyya, Sudeepa; and Prior, Fred, "DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool" (2021). Arkansas Biosciences Institute. 46.
https://arch.astate.edu/abi/46
