A learning health system in Neurorehabilitation as a foundation for multimodal patient representation

TL;DR

This paper presents a learning health system framework for neurorehabilitation that integrates multimodal data, computational models, and visualization to enhance clinician-ML collaboration and facilitate translational research.

Contribution

It introduces an integrated infrastructure embedding the LHS framework in neurorehabilitation, enabling structured data collection, secure processing, and visualization for clinical use.

Findings

Demonstrated real-world deployment in stroke rehabilitation.

Bridged gap between research models and clinical practice.

Enabled clinician-ML collaboration in neurorehabilitation.

Abstract

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.