Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing.

Journal: BMC medical informatics and decision making
Published Date: Jul 12, 2021

Abstract

BACKGROUND: A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of accurate automated protocol assignment. We aim to develop, evaluate, and deploy an NLP model that automates protocol assignment, given the clinician indication text.

Authors

  • Yeshwant Reddy Chillakuru
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave, San Francisco, CA, 94143, USA.
  • Shourya Munjal
    Radiology & Biomedical Imaging, University of California San Francisco (UCSF), 505 Parnassus Ave, San Francisco, CA, 94158, USA.
  • Benjamin Laguna
    Radiology & Biomedical Imaging, UCSF Medical Center, 505 Parnassus Ave, San Francisco, CA, 94158, USA.
  • Timothy L Chen
    University of California San Francisco (UCSF), Radiology and Biomedical Imaging, 505 Parnassus Ave, San Francisco, CA 94143, USA; University of Illinois College of Medicine, 1853 W Polk St, Chicago, IL 60612, USA.
  • Gunvant R Chaudhari
    University of California San Francisco (UCSF), Radiology and Biomedical Imaging, 505 Parnassus Ave, San Francisco, CA 94143, USA.
  • Thienkhai Vu
    Radiology & Biomedical Imaging, UCSF Medical Center, 505 Parnassus Ave, San Francisco, CA, 94158, USA.
  • Youngho Seo
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
  • Jared Narvid
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
  • Jae Ho Sohn
    Radiology & Biomedical Imaging, UCSF Medical Center, 505 Parnassus Ave, San Francisco, CA, 94158, USA. sohn87@gmail.com.

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