Reinforcement Learning (RL) for Individualized Speech–Language Therapy
Reinforcement Learning
Clinical Decision Support
Speech-Language Therapy
Personalization
Overview
This stream builds reinforcement-learning systems embedded in clinician workflows to personalize and optimize speech–language therapy, improving both treatment efficiency and child outcomes while preserving clinician oversight.
What we do
We model therapy adaptation as a sequential decision process and develop human-in-the-loop systems that provide recommendations while keeping clinicians in control.
Key components
- Reinforcement learning models of therapy adaptation
- Human-in-the-loop decision support
- Workflow integration for clinicians
- Optimization of outcomes and efficiency