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arXiv · 2009.05128

RadLex Normalization in Radiology Reports

Surabhi Datta·Jordan Godfrey-Stovall·Kirk Roberts
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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.

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Surabhi Datta, Jordan Godfrey-Stovall, Kirk Roberts. 2020-09-10. RadLex Normalization in Radiology Reports. https://arxiv.org/abs/2009.05128

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