Background Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the “right intervention, at the right time, in the right setting, for the right person,” is essential for optimizing stroke recovery. Although Artificial Intelligence (AI) has been effectively utilized in other medical fields, such as cancer and sepsis treatments, no current AI system is designed to tailor and continuously refine rehabilitation plans post-stroke.

Methods We propose a novel AI-based decision-support system for precision rehabilitation that uses Reinforcement Learning (RL) to personalize the treatment plan. Specifically, our system iteratively adjusts the sequential treatment plan—timing, dosage, and intensity— to maximize long-term outcomes based on a patient model that includes covariate data (the context). The system collaborates with clinicians and people with stroke to customize the recommended plan based on clinical judgment, constraints, and preferences. To achieve this goal, we propose a Contextual Markov Decision Process (CMDP) framework and a novel hierarchical Bayesian model-based RL algorithm, named Posterior Sampling for Contextual RL (PSCRL), that discovers and continuously adjusts near-optimal sequential treatments by efficiently balancing exploitation and exploration while respecting constraints and preferences.

Results We implemented and validated our precision rehabilitation system in simulations with a sequence of 100 diverse, synthetic patients. Simulation results showed the system ability to continuously learn from both upcoming data from the current patient and a database of past patients via Bayesian hierarchical modeling. Specifically, the algorithm’s sequential treatment recommendations became increasingly more effective in improving functional gains for each patient over time and across the synthetic patient population.

Conclusions Our novel AI-based precision rehabilitation system based on contextual model-based reinforcement learning has the potential to play a key role in novel learning health systems in rehabilitation.

The authors have declared no competing interest.

This work was funded by grant NIH R56 NS126748 to NS.

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