RESUMO
Speech language pathologists need an accurate assessment of the severity of people with aphasia (PWA) to design and provide the best course of therapy. Currently, severity is evaluated manually by an increasingly scarce pool of experienced and well-trained clinicians, taking considerable time resources. By analyzing the transcripts from three discourse elicitation methods, this study combines natural language processing (NLP) and machine learning (ML) to predict the severity of PWA, both by score and severity level. By engineering language features from PWA tasks, an unstructured k-means clustering presents distinct aphasia types, showing validity of the selected features. We develop regression models to predict severity scores along with a classification of severity by level (Mild, Moderate, Severe, and Very Severe) to assist clinicians to easily plan and monitor the course of treatment. Our best ML regression model uses a deep neural network and results in a mean absolute error (MAE) of 0.0671 and root mean squared error (RMSE) of 0.0922. Our best classification model uses a random forest and result in an overall accuracy of 73%, with the highest accuracy of 87.5% for mild severity. Our results suggest that using NLP and ML provides an accurate and cost-effective approach to evaluate the severity levels in PWA to consequently help clinicians determine rehabilitation procedures.
