Document Type
Article
Publication Date
11-1-2018
Description
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Citation Information
Orimaye, Sylvester Olubolu; Wong, Jojo Sze Meng; and Wong, Chee Piau. 2018. Deep Language Space Neural Network for Classifying Mild Cognitive Impairment and Alzheimer-Type Dementia. PLoS ONE. Vol.13(11). https://doi.org/10.1371/journal.pone.0205636 PMID: 30403676
Copyright Statement
Copyright: © 2018 Orimaye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.