Abstract
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.
Highlights
Diagnosis of Mild Cognitive Impairment due to Alzheimer’s disease (MCI) and Alzheimer-type dementia (AD-type dementia) is currently a challenge [1] [2]
We explore deep-deep neural networks language models (D2NNLM) to learn the linguistic changes that distinguish the language of patients with MCI and AD-type dementia from the healthy controls using higher order n-grams
(1) We introduce deep language models in the form of decomposed higher order n-grams N dimensional vectors as discrete inputs to the deep neural network (DNN), we derived D2NNLM. (2) We show that D2NNLM predicts MCI and AD-type dementia with less percentage error, perplexity, and Area Under the ROC Curve (AUC), especially on sparse clinical language datasets
Summary
Diagnosis of Mild Cognitive Impairment due to Alzheimer’s disease (MCI) and Alzheimer-type dementia (AD-type dementia) is currently a challenge [1] [2] Both MCI and AD-type dementia have been typically diagnosed through extensive neuropsychological examinations using a series of cognitive tests containing a set of questions and images [3]. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) screening tools are composed of a series of questions and cognitive tests, that assess different cognitive abilities The challenges with these cognitive tests are that the accuracy depends on the clinician’s level of experience and their ability to diagnose different sub-types of the disease [4].
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