Investigating the role of a mid-term clinical assessment in the prediction of motor recovery in post-stroke survivors
Year: 2024
Type: conference-paper
Abstract: This research explores the impact of incorporating mid-term clinical assessments on predicting robotic rehabilitation outcomes for post-stroke patients using Random Forest models. While predicting motor recovery is complex and critical for personalizing therapies and reducing healthcare costs, adding mid-term assessments yielded limited improvements in prediction accuracy. The study compared predictions using only initial assessments versus both initial and mid-term assessments for three clinical scales: FMA, ARAT, and MI. Results showed that only ARAT predictions benefited from mid-term data, suggesting that further analysis is needed to fully understand the potential of mid-term assessments in enhancing predictive models.
Authors: Tzepkenlis, Anastasios, Camardella, Cristian, Frisoli, Antonio
Language: English
Subfield: Rehabilitation
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6968337646
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