A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models
<|reference_start|>A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models: Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstructured narrative must be converted into a structured semantic representation to facilitate secondary applications such as retrospective analyses or clinical decision support. Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography. Reports were annotated using an event-based schema that captures clinical indications, lesions, and medical problems. Each event consists of a trigger and multiple arguments, and a majority of the argument types, including anatomy, normalize the spans to pre-defined concepts to facilitate secondary use. CAMIR uniquely combines a granular event structure and concept normalization. To extract CAMIR events, we explored two BERT (Bi-directional Encoder Representation from Transformers)-based architectures, including an existing architecture (mSpERT) that jointly extracts all event information and a multi-step approach (PL-Marker++) that we augmented for the CAMIR schema.<|reference_end|>
@article{park2024a,
    title={A Novel Corpus of Annotated Medical Imaging Reports and Information
  Extraction Results Using BERT-based Language Models},
    author={Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer
  Lewis, Aashka Damani, Ozlem Uzuner, Martin Gunn, Meliha Yetisgen},
    journal={arXiv preprint arXiv:2403.18975},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18975},
    primaryClass={cs.CL}
}