A Novel Natural Language Processing Model for Triaging Head and Neck Patient Appointments
This novel NLP model demonstrated robust performance characteristics for predicting H&N diagnoses based on referring documents and excelled at identifying patients requiring urgent care based on malignancy risk. This tool may help H&N practice coordinators screen referrals, potentially optimizing patient care.
Authors
- Wojciech K. Mydlarz (ORCID: https://orcid.org/0000-0002-4105-7092)
- Wayne M. Koch (ORCID: https://orcid.org/0000-0002-9499-1516)
- Nyall R. London (ORCID: https://orcid.org/0000-0002-2902-4407)
- Andy S. Ding (ORCID: https://orcid.org/0000-0002-8490-057X)
- Leila J. Mady (ORCID: https://orcid.org/0000-0002-2682-3297)
- Carole Fakhry (ORCID: https://orcid.org/0000-0002-5251-6178)
- Christine G. Gourin (ORCID: https://orcid.org/0000-0001-5134-9307)
- Nicole T. Jiam (ORCID: https://orcid.org/0000-0001-9838-5065)
- Stefanie Seo
- Syed Ameen Ahmad (ORCID: https://orcid.org/0000-0002-6179-5252)
- Max L. Jiam
- Kevin Xin (ORCID: https://orcid.org/0009-0007-5745-7115)
- Vincent Xin
Institutions
- Johns Hopkins University (US)
- University of California, San Francisco (US)
- University of California, Irvine (US)
- Johns Hopkins Medicine (US)
- Abeam Technologies (United States) (US)
- Stanford University (US)
Publication Details
- Journal
- Otolaryngology
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1002/ohn.1244
- Primary Topic
- Healthcare Systems and Technology
- Type
- article
- Field-Weighted Citation Impact
- 0.00