Vector-space topic models for detecting Alzheimer's disease
Maria Yancheva, Frank Rudzicz · 2016
Semantic deficit is a symptom of language impairment in Alzheimer's disease (AD).We present a generalizable method for automatic generation of information content units (ICUs) for a picture used in a standard clinical task, achieving high recall, 96.8%, of human-supplied ICUs.We use the automatically generated topic model to extract semantic features, and train a random forest classifier to achieve an F-score of 0.74 in binary classification of controls versus people with AD using a set of only 12 features.This is comparable to results (0.72 F-score) with a set of 85 manual features.Adding semantic information to a set of standard lexicosyntactic and acoustic features improves F-score to 0.80.While control and dementia subjects discuss the same topics in the same contexts, controls are more informative per second of speech.