Machine Learning Driven 'Therapy Calculator' for Self-Managed Digital Speech-Language Therapy for Individuals with Post-stroke Aphasia

Individuals with post-stroke aphasia live with long-term disabilities, yet they do not know whether they will improve their communication and cognitive skills over time. We propose a "Therapy Calculator" to provide patients with a better understanding of likely recovery as they engage with therapy. Using a large dataset of rehabilitation outcomes from a digital therapeutic called Constant Therapy (3.5 million therapy sessions of 18,000+ users), we developed a machine learning algorithm that estimates the probability of improvement from one functional landmark (i.e., a given skill level) to the next in a functional domain (e.g., reading) while accounting for age, etiology, starting performance, and frequency and duration of therapy. This logistic regression model performed a binary classification task, i.e., whether patients can improve to the next landmark, with an average F1 score of all models at 0.84, suggesting reliable prediction of moving to the next landmark. Then, we created an online "Therapy Calculator" to assess a new users current functional level and demographic information, and make predictions by passing these features into models trained on relevant subsets of historical data. The findings indicate that our model can provide reliable predictions for patients beginning self-managed SLT, and therapy calculator is publicly available.