Automation of Protocoling Advanced MSK Examinations Using Natural Language Processing Techniques.
Year: 2023
Type: article
Abstract: Imaging examination selection and protocoling are vital parts of the radiology workflow, ensuring that the most suitable exam is done for the clinical question while minimizing the patient's radiation exposure. In this study, we aimed to develop an automated model for the revision of radiology examination requests using natural language processing techniques to improve the efficiency of pre-imaging radiology workflow. We extracted Musculoskeletal (MSK) magnetic resonance imaging (MRI) exam order from the radiology information system at Henry Ford Hospital in Detroit, Michigan. The pretrained transformer, "DistilBERT" was adjusted to create a vector representation of the free text within the orders while maintaining the meaning of the words. Then, a logistic regression-based classifier was trained to identify orders that required additional review. The model achieved 83% accuracy and had an area under the curve of 0.87.
Source: PubMed Central
Institutions: Michigan State University, Henry Ford Hospital
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4381715717