Using linguistic features longitudinally to predict clinical scores for alzheimer s disease and related dementias

Abstract

We use a set of 477 lexicosyntactic, acoustic, and semantic features extracted from 393 speech samples in DementiaBank to predict clinical MMSE scores, an indicator of the severity of cognitive decline associated with dementia. We use a bivariate dynamic Bayes net to represent the longitudinal progression of observed linguistic features and MMSE scores over time, and obtain a mean absolute error (MAE) of 3.83 in predicting MMSE, comparable to within-subject interrater standard deviation of 3.9 to 4.8 [1]. When focusing on individuals with more longitudinal samples, we improve MAE to 2.91, which suggests at the importance of longitudinal data collection.