BI-RADS BERT & Using Section Tokenization to Understand Radiology Reports

• URL: http://arxiv.org/abs/2110.07552v1
• Date: Thu, 14 Oct 2021 17:25:49 GMT
• Title: BI-RADS BERT & Using Section Tokenization to Understand Radiology Reports
• Authors: Grey Kuling, Dr. Belinda Curpen, and Anne L. Martel
• Abstract summary: Domain specific contextual word embeddings have been shown to achieve impressive accuracy at such natural language processing tasks in medicine. BERT model pre-trained on breast radiology reports combined with section tokenization resulted in an overall accuracy of 95.9% in field extraction.
• Score: 0.18352113484137625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radiology reports are the main form of communication between radiologists and other clinicians, and contain important information for patient care. However in order to use this information for research it is necessary to convert the raw text into structured data suitable for analysis. Domain specific contextual word embeddings have been shown to achieve impressive accuracy at such natural language processing tasks in medicine. 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 a section tokenization task. This model achieved a 98% accuracy at segregating free text reports into sections of information outlined in the Breast Imaging Reporting and Data System (BI-RADS) lexicon, a significant improvement over the Classic BERT model without auxiliary information. We then evaluated whether using section tokenization improved the downstream extraction of the following fields: modality/procedure, previous cancer, menopausal status, purpose of exam, breast density and background parenchymal enhancement. Using the BERT model pre-trained on breast radiology reports combined with section tokenization resulted in an overall accuracy of 95.9% in field extraction. This is a 17% improvement compared to an overall accuracy of 78.9% for field extraction for models without section tokenization and with Classic BERT embeddings. Our work shows the strength of using BERT in radiology report analysis and the advantages of section tokenization in identifying key features of patient factors recorded in breast radiology reports.

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