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To collect large-scale, high-quality clinical data, daily activity and movement patterns, and genomic information from stroke patients spanning the subacute to chronic stages.
This study aims to establish the foundation for personalized precision medicine in stroke care and to develop a visual and digital transformation of patient medical information in the era of digital healthcare.
Full description
This study aims to establish a comprehensive, high-quality dataset that captures the longitudinal characteristics of stroke recovery from the subacute to chronic stages. By integrating clinical data, daily activity and movement patterns, and genomic information, the study reflects the multidimensional nature of stroke in a real-world setting.
The collected data will support data-driven approaches in stroke care by enabling a better understanding of individual variability in recovery trajectories and facilitating the development of predictive models for functional outcomes.
In addition, this study seeks to contribute to personalized precision medicine by enabling individualized interpretation of patient data. It also supports the digital transformation of healthcare by facilitating structured, visual, and data-driven representations of patient medical information for improved clinical and research applications.
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70 participants in 1 patient group
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Seyoung Shin, MD
Data sourced from clinicaltrials.gov
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