Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression
The novel use of a NN for this purpose, and successful benchmarking against conventional techniques, justifies further exploration and expansion of this model.
Authors
- Robert Jeraj (ORCID: https://orcid.org/0000-0002-2192-2931)
- Michael J. McKenna (ORCID: https://orcid.org/0000-0001-9998-0093)
- Jonathan Palumbo
- Luigi Ferrucci (ORCID: https://orcid.org/0000-0002-6273-1613)
- Alison Deatsch (ORCID: https://orcid.org/0000-0002-4525-1327)
- Qu Tian (ORCID: https://orcid.org/0000-0003-2706-1439)
- Richard G. Spencer (ORCID: https://orcid.org/0000-0001-7101-4328)
- Eleanor Marie Simonsick (ORCID: https://orcid.org/0000-0002-5460-3494)
Institutions
- National Institutes of Health (US)
- University of Wisconsin–Madison (US)
- University of Ljubljana (SI)
- Institute on Aging (US)
- National Institute on Aging (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1371/journal.pone.0325172
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00