Detecting Alzheimer's Disease Using Natural Language Processing of Referential Communication Task Transcripts.

Abstract

People with Alzheimer's disease (AD) often demonstrate difficulties in discourse production. Referential communication tasks (RCTs) are used to examine a speaker's capability to select and verbally code the characteristics of an object in interactive conversation. In this study, we used contextualized word representations from Natural language processing (NLP) to evaluate how well RCTs are able to distinguish between people with AD and cognitively healthy older adults. We adapted machine learning techniques to analyze manually transcribed speech transcripts in an RCT from 28 older adults, including 12 with AD and 16 cognitively healthy older adults. Two approaches were applied to classify these speech transcript samples: 1) using clinically relevant linguistic features, 2) using machine learned representations derived by a state-of-art pretrained NLP transfer learning model, Bidirectional Encoder Representation from Transformer (BERT) based classification model. The results demonstrated the superior performance of AD detection using a designed transfer learning NLP algorithm. Moreover, the analysis showed that transcripts of a single image yielded high accuracies in AD detection. The results indicated that RCT may be useful as a diagnostic tool for AD, and that the task can be simplified to a subset of images without significant sacrifice to diagnostic accuracy, which can make RCT an easier and more practical tool for AD diagnosis. The results also demonstrate the potential of RCT as a tool to better understand cognitive deficits from the perspective of discourse production in people with AD.