Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model

• URL: http://arxiv.org/abs/2108.09211v1
• Date: Fri, 20 Aug 2021 15:02:59 GMT
• Title: Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model
• Authors: Kevin Lybarger, Aashka Damani, Martin Gunn, Ozlem Uzuner, Meliha Yetisgen
• Abstract summary: Medical imaging reports distill the findings and observations of radiologists. Large-scale use of this text-encoded information requires converting the unstructured text to a structured, semantic representation. We explore the extraction and normalization of anatomical information in radiology reports that is associated with radiological findings.
• Score: 0.20999222360659603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical imaging is critical to the diagnosis and treatment of numerous medical problems, including many forms of cancer. Medical imaging reports distill the findings and observations of radiologists, creating an unstructured textual representation of unstructured medical images. Large-scale use of this text-encoded information requires converting the unstructured text to a structured, semantic representation. We explore the extraction and normalization of anatomical information in radiology reports that is associated with radiological findings. We investigate this extraction and normalization task using a span-based relation extraction model that jointly extracts entities and relations using BERT. This work examines the factors that influence extraction and normalization performance, including the body part/organ system, frequency of occurrence, span length, and span diversity. It discusses approaches for improving performance and creating high-quality semantic representations of radiological phenomena.

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