Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis.

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Bibliographic Details
Title: Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis.
Authors: Hu W; Ubiquitous Computing and Machine Learning Research Lab, Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada., Combden O; Ubiquitous Computing and Machine Learning Research Lab, Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada., Jiang X; Ubiquitous Computing and Machine Learning Research Lab, Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada., Buragadda S; Recovery and Performance Laboratory, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada., Newell CJ; Recovery and Performance Laboratory, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada., Williams MC; Recovery and Performance Laboratory, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada., Critch AL; Recovery and Performance Laboratory, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada., Ploughman M; Recovery and Performance Laboratory, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.
Source: Frontiers in artificial intelligence [Front Artif Intell] 2022 Sep 29; Vol. 5, pp. 952312. Date of Electronic Publication: 2022 Sep 29 (Print Publication: 2022).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media SA Country of Publication: Switzerland NLM ID: 101770551 Publication Model: eCollection Cited Medium: Internet ISSN: 2624-8212 (Electronic) Linking ISSN: 26248212 NLM ISO Abbreviation: Front Artif Intell Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2624-8212
DOI:10.3389/frai.2022.952312