Language-Based Automatic Assessment of Cognitive and Communicative Functions Related to Parkinson's Disease

Abstract

We explore the use of natural language processing and machine learning for detecting evidence of Parkinson's disease from transcribed speech of subjects who are describing everyday tasks. Experiments reveal the difficulty of treating this as a binary classification task, and a multi-class approach yields superior results. We also show that these models can be used to predict cognitive abilities across all subjects.

Tasks

Reproductions