Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BI-RADS BERT & Using Section Segmentation to Understand Radiology
  Reports   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 model achieved a 98% accuracy at
segregating free text reports sentence by sentence 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 segmentation improved the
downstream extraction of clinically relevant information such as
modality/procedure, previous cancer, menopausal status, the purpose of the
exam, breast density, and breast MRI background parenchymal enhancement. Using
the BERT model pre-trained on breast radiology reports combined with section
segmentation resulted in an overall accuracy of 95.9% in the field extraction
tasks. This is a 17% improvement compared to an overall accuracy of 78.9% for
field extraction with models using Classic BERT embeddings and not using
section segmentation. Our work shows the strength of using BERT in radiology
report analysis and the advantages of section segmentation in identifying key
features of patient factors recorded in breast radiology reports.

11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: math-ph