Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models
An NLP-based automated referral system can accurately identify patients with bone metastases requiring specialist evaluation. A simple RegEx model excels in syntax-based identification and expert-informed rule generation for efficient referral patient recommendation in comparison with advanced NLP models. This system could significantly reduce missed follow-ups and enhance timely intervention for patients with bone metastases.
Authors
- Anton S. Becker (ORCID: https://orcid.org/0000-0001-8372-6496)
- Anton Alyakin
- Douglas Kondziolka (ORCID: https://orcid.org/0000-0001-8925-7344)
- Richard Ni
- Daniel Alexander Alber (ORCID: https://orcid.org/0000-0001-7957-5170)
- Karl L. Sangwon (ORCID: https://orcid.org/0000-0002-6941-3001)
- Ilya Laufer (ORCID: https://orcid.org/0000-0003-0092-5030)
- Abraham Chachoua
- Eric K. Oermann (ORCID: https://orcid.org/0000-0002-1876-5963)
- Jonathan T. Yang (ORCID: https://orcid.org/0000-0001-9375-7384)
- Yuchong Zhang (ORCID: https://orcid.org/0000-0003-1804-6296)
- Masami Nakatsuka
- Yindalon Aphinyanaphongs (ORCID: https://orcid.org/0000-0001-8605-5392)
- Xu Han (ORCID: https://orcid.org/0000-0002-8967-4372)
- Nicola Fabbri
- Jeff Zhang
Institutions
- Washington University in St. Louis (US)
- NYU Langone Health (US)
- Neurological Surgery (US)
- New York University (US)
Publication Details
- Journal
- Neurosurgery
- Published
- 2025-08-15
- DOI
- https://doi.org/10.1227/neu.0000000000003683
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72