  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.

Computation and Language