Vector-space topic models for detecting Alzheimer's disease

Maria Yancheva, Frank Rudzicz
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
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
more » ... D 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.
doi:10.18653/v1/p16-1221 dblp:conf/acl/YanchevaR16 fatcat:37ya7pwhzvdxnlx5y7rfeg3ylm