Version 1
: Received: 15 July 2024 / Approved: 15 July 2024 / Online: 15 July 2024 (11:31:32 CEST)
How to cite:
Adikari, A.; Nawaratne, R.; De Silva, D.; Carey, D.; Walsh, A.; Baum, C.; Davis, S.; Donnan, G. A.; (PhD), D.; Carey, L. M. Is Mild Really Mild?”: Generating Longitudinal Profiles of Stroke Survivor Impairment and Impact Using Unsupervised Machine Learning. Preprints2024, 2024071156. https://doi.org/10.20944/preprints202407.1156.v1
Adikari, A.; Nawaratne, R.; De Silva, D.; Carey, D.; Walsh, A.; Baum, C.; Davis, S.; Donnan, G. A.; (PhD), D.; Carey, L. M. Is Mild Really Mild?”: Generating Longitudinal Profiles of Stroke Survivor Impairment and Impact Using Unsupervised Machine Learning. Preprints 2024, 2024071156. https://doi.org/10.20944/preprints202407.1156.v1
Adikari, A.; Nawaratne, R.; De Silva, D.; Carey, D.; Walsh, A.; Baum, C.; Davis, S.; Donnan, G. A.; (PhD), D.; Carey, L. M. Is Mild Really Mild?”: Generating Longitudinal Profiles of Stroke Survivor Impairment and Impact Using Unsupervised Machine Learning. Preprints2024, 2024071156. https://doi.org/10.20944/preprints202407.1156.v1
APA Style
Adikari, A., Nawaratne, R., De Silva, D., Carey, D., Walsh, A., Baum, C., Davis, S., Donnan, G. A., (PhD), D., & Carey, L. M. (2024). Is Mild Really Mild?”: Generating Longitudinal Profiles of Stroke Survivor Impairment and Impact Using Unsupervised Machine Learning. Preprints. https://doi.org/10.20944/preprints202407.1156.v1
Chicago/Turabian Style
Adikari, A., Damminda (PhD) and Leeanne M Carey. 2024 "Is Mild Really Mild?”: Generating Longitudinal Profiles of Stroke Survivor Impairment and Impact Using Unsupervised Machine Learning" Preprints. https://doi.org/10.20944/preprints202407.1156.v1
Abstract
The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’ or ‘severe’ based on neurological impairment. Yet stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment among stroke survivors classified as mild. We used mild stroke survivors’ data from the START longitudinal stroke cohort (n=73) with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work and social adjustment over 12 months. Given the multi-source, multi-granular and unlabelled nature of data, we utilised a structure-adapting, unsupervised machine learning approach, the Growing Self-organising Map (GSOM) algorithm to generate distinct clinical profiles. These diverse impairment profiles revealed that mild stroke survivors experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified ‘mild’ stroke. This emphasises the necessity of creating a holistic and comprehensive representation of mild stroke survivors’ needs over the first-year post-stroke to improve rehabilitation and post-stroke care.
Public Health and Healthcare, Physical Therapy, Sports Therapy and Rehabilitation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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