A learning health system in Neurorehabilitation as a foundation for multimodal patient representation
This work addresses the gap between computational models and clinical practice in neurorehabilitation by providing an infrastructure for data-driven personalization, but the results are preliminary and incremental.
The authors developed a learning health system for neurorehabilitation that integrates multimodal data collection, model computation, and clinical visualization to enable clinician-ML collaboration, and demonstrated its feasibility in a real-world stroke rehabilitation deployment.
Neurological disorders represent a growing global health burden requiring long-term, interdisciplinary rehabilitation. Computational neurorehabilitation (compNR) - the use of data-driven and model-based approaches to personalize treatment - offers new opportunities for precision rehabilitation. However, its clinical deployment is limited by fragmented data systems, poor interoperability, and low clinician engagement in model development. We embed the learning health system (LHS) framework in Neurorehabilitation through integration of multimodal data collection, model computation, and clinical visualization that enables clinician-ML collaboration in everyday neurorehabilitation practice. The system facilitates structured digital data capture, secure computational processing, and interoperable visualization of patient trajectories. Through a real-world deployment in stroke rehabilitation, we demonstrate how such an infrastructure bridges the gap between research models and clinical use, showcasing one approach to a translational pathway for compNR.