Leveraging Large Language Models to Derive Multiple Sclerosis Progression Assessments from Clinical Notes: A Feasibility Study
Ascertainment of multiple sclerosis (MS) progression is important for informing clinical care decisions and supporting biomedical research. However, the details to infer a patient's MS progression status are locked within clinical notes. In this feasibility study, we assessed the feasibility of developing and validating a large language model (LLM)-based EDSS and FS classifier for ascertaining MS progression from clinical notes.
Authors
- Sy Hwang (ORCID: https://orcid.org/0000-0003-3851-9521)
- Christopher Perrone (ORCID: https://orcid.org/0000-0002-8604-2951)
- Ashley Batugo
- Vishakha Sharma (ORCID: https://orcid.org/0000-0002-9677-2325)
- Emily Schriver (ORCID: https://orcid.org/0000-0003-4522-1029)
- Danielle L. Mowery (ORCID: https://orcid.org/0000-0003-3802-4457)
- Heather Williams (ORCID: https://orcid.org/0000-0002-5079-1328)
- Sunil Thomas
- Frederik Buijs
- Amit Bar-Or (ORCID: https://orcid.org/0000-0001-7179-0335)
- Tom Hutchinson (ORCID: https://orcid.org/0000-0002-1002-0449)
Institutions
- Roche (Switzerland) (CH)
- Roche (United States) (US)
- University of Pennsylvania (US)
Publication Details
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1142/9789819824755_0023
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00