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http://hdl.handle.net/1942/36270
Title: | Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning | Authors: | Kanzler, Christoph M. LAMERS, Ilse FEYS, Peter Gassert, Roger Lambercy, Olivier |
Issue Date: | 2022 | Publisher: | SPRINGER HEIDELBERG | Source: | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 60(1), p. 249-261 | Abstract: | Predicting upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) is essential to optimize therapy allocation. Previous research identified population-level predictors through linear models and clinical data. This work explores the feasibility of predicting individual neurorehabilitation outcomes using machine learning, clinical data, and digital health metrics. Machine learning models were trained on clinical data and digital health metrics recorded pre-intervention in 11 pwMS. The dependent variables indicated whether pwMS considerably improved across the intervention, as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT). Improvements in ARAT or BBT could be accurately predicted (88% and 83% accuracy) using only patient master data. Improvements in NHPT could be predicted with moderate accuracy (73%) and required knowledge about sensorimotor impairments. Assessing these with digital health metrics over clinical scales increased accuracy by 10%. Non-linear models improved accuracy for the BBT (+ 9%), but not for the ARAT (-1%) and NHPT (-2%). This work demonstrates the feasibility of predicting upper limb neurorehabilitation outcomes in pwMS, which justifies the development of more representative prediction models in the future. Digital health metrics improved the prediction of changes in hand control, thereby underlining their advanced sensitivity. | Notes: | Kanzler, CM (corresponding author), Swiss Fed Inst Technol, Dept Hlth Sci & Technol, Inst Robot & Intelligent Syst, Rehabil Engn Lab, BAA C 307-1,Lengghalde 5, CH-8008 Zurich, Switzerland.; Kanzler, CM (corresponding author), Singapore ETH Ctr, Campus Res Excellence & Technol Enterprise, Future Hlth Technol, Singapore, Singapore. christoph.kanzler@hest.ethz.ch |
Keywords: | Prognostic factors; Neurorehabilitation; Digital biomarkers; Assessment;;Upper limb | Document URI: | http://hdl.handle.net/1942/36270 | ISSN: | 0140-0118 | e-ISSN: | 1741-0444 | DOI: | 10.1007/s11517-021-02467-y | ISI #: | WOS:000722473100002 | Rights: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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s11517-021-02467-y.pdf | Published version | 458.07 kB | Adobe PDF | View/Open |
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