Application of Machine Learning to Derive Key Indices of MS-related Function from Low-burden Digital Speech and Gait Tasks (P2-1.005)
To leverage machine learning (ML) to develop, from a low-burden suite of in-clinic digital tasks, algorithms that can recapitulate gold standard functional assessments in MS.
Authors
- Adam M. Staffaroni (ORCID: https://orcid.org/0000-0002-3903-9805)
- Johannes Tröger (ORCID: https://orcid.org/0000-0003-3404-0126)
- Ethan Brown (ORCID: https://orcid.org/0000-0001-9744-8562)
- Valerie J. Block (ORCID: https://orcid.org/0000-0001-6199-5484)
- Alyssa N. Nylander (ORCID: https://orcid.org/0000-0002-1976-3847)
- Jaeleene Wijangco (ORCID: https://orcid.org/0009-0006-1561-6547)
- Marcelo Fernandes Pacheco Dias (ORCID: https://orcid.org/0000-0003-4610-0819)
- Cathra Halabi (ORCID: https://orcid.org/0000-0002-0331-8183)
- Alexandra König (ORCID: https://orcid.org/0000-0001-9960-9657)
- Shane T. Poole (ORCID: https://orcid.org/0000-0002-3203-151X)
- Riley M. Bove (ORCID: https://orcid.org/0000-0002-2034-8800)
- Nicklas Linz (ORCID: https://orcid.org/0000-0001-5178-3234)
- Nikki Sisodia (ORCID: https://orcid.org/0009-0006-7270-595X)
- Kyra Henderson
Institutions
- University of California, San Francisco (US)
- Universidad Católica de Santa Fe (AR)
- Universitetshospitalernes Center for Sundhedsfaglig Forskning (DK)
Publication Details
- Journal
- Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1212/wnl.0000000000212386
- Primary Topic
- Educational Technology and Pedagogy
- Type
- article
- Field-Weighted Citation Impact
- 0.00