Automation of Protocoling Advanced MSK Examinations Using Natural Language Processing Techniques
Recommended Citation
Eghbali N, Siegal D, Klochko C, and Ghassemi MM. Automation of Protocoling Advanced MSK Examinations Using Natural Language Processing Techniques. AMIA Jt Summits Transl Sci Proc 2023; 2023:118-127.
Document Type
Article
Publication Date
6-1-2023
Publication Title
AMIA Jt Summits Transl Sci Proc
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.
PubMed ID
37350898
Volume
2023
First Page
118
Last Page
127
