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

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.

Pseudo-data generation for the extraction of Problems, Treatments and Tests.

One of the primary challenges for clinical Named Entity Recognition (NER) is the availability of annotated training data. Technical and legal hurdles prevent the creation and release of corpora related to electronic health records (EHRs). In this work, we look at the impact of pseudo-data generation on clinical NER using gazetteering utilizing a neural network model. We report that gazetteers can result in the inclusion of proper terms with the exclusion of determiners and pronouns in preceding and middle positions. Gazetteers that had higher numbers of terms inclusive to the original dataset had a higher impact.

Radiologic findings in a patient with frontal parafalcine dendritic cell histiocytoma.

We report the case history and radiologic findings of a patient with a biopsy-proven dendritic cell histiocytoma presenting as a single intracranial extra-axial mass and no systemic disease. Even though this entity is relatively rare, it should nevertheless be considered in the differential diagnosis of dural-based space-occupying central nervous system lesions.