BI-RADS BERT and Using Section Segmentation to Understand Radiology Reports.

ABSTRACT: Radiology reports are one of the main forms of communication between radiologists and other clinicians, and contain important information for patient care. In order to use this information for research and automated patient care programs, it is necessary to convert the raw text into structured data suitable for analysis. State-of-the-art natural language processing (NLP) domain-specific contextual word embeddings have been shown to achieve impressive accuracy for these tasks in medicine, but have yet to be utilized for section structure segmentation. In this work, we pre-trained a contextual embedding BERT model using breast radiology reports and developed a classifier that incorporated the embedding with auxiliary global textual features in order to perform section segmentation. This mode

SUBMITTER: Kuling G 

PROVIDER: S-EPMC9148091 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature