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WSC 2022 | Machine learning in post-stroke rehabilitation

Vinicius V. Montanaro, MD, MSc, PhD, SARAH Network of Rehabilitation Hospitals, Brasília, Brazil, talks on valuable applications of machine learning in stroke care and rehabilitation. Over the last decade, research groups across the globe have been developing and optimizing machine learning models for outcome prediction. The deep analysis and pattern recognition that can be achieved with machine learning has brought about substantial improvements in outcome prediction and thus, optimal care planning. Machine learning has also been employed for data driven rehabilitation. At home data collection to monitor care effectiveness, automated analysis for a more accurate patient assessment, recognition of activities of daily living through wearable sensors, and classification of tasks into well and poorly executed have all been used to improve stroke rehabilitation. This interview took place at the World Stroke Congress 2022 in Singapore.

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