Open AccessDeep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia
Author(s) -
Sylvester Olubolu Orimaye,
Jojo Sze-Meng Wong,
Chee Piau Wong
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0205636
Subject(s) - dementia , neuropsychology , artificial intelligence , alzheimer's disease , cognition , artificial neural network , computer science , disease , psychology , natural language processing , medicine , cognitive psychology , neuroscience , pathology
It has been quite a challenge to diagnose Mild Cognitive Impairment due to Alzheimer’s disease (MCI) and Alzheimer-type dementia (AD-type dementia) using the currently available clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of affected individuals. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) for classifying the disease. Results on the DementiaBank language transcript clinical dataset show that D2NNLM sufficiently learned several linguistic biomarkers in the form of higher order n -grams to distinguish the affected group from the healthy group with reasonable accuracy on very sparse clinical datasets.