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RadLex Normalization in Radiology Reports.
- Source :
-
AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2021 Jan 25; Vol. 2020, pp. 338-347. Date of Electronic Publication: 2021 Jan 25 (Print Publication: 2020). - Publication Year :
- 2021
-
Abstract
- Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabulary. Further, no study to date has attempted to leverage RadLex for standardization. In this paper, we aim to normalize a diverse set of radiological entities to RadLex terms. We manually construct a normalization corpus by annotating entities from three types of reports. This contains 1706 entity mentions. We propose two deep learning-based NLP methods based on a pre-trained language model (BERT) for automatic normalization. First, we employ BM25 to retrieve candidate concepts for the BERT-based models (re-ranker and span detector) to predict the normalized concept. The results are promising, with the best accuracy (78.44%) obtained by the span detector. Additionally, we discuss the challenges involved in corpus construction and propose new RadLex terms.<br /> (©2020 AMIA - All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1942-597X
- Volume :
- 2020
- Database :
- MEDLINE
- Journal :
- AMIA ... Annual Symposium proceedings. AMIA Symposium
- Publication Type :
- Academic Journal
- Accession number :
- 33936406