Abstract
Language deficiency is evident in the onset of several neurodegenerative disorders yet has barely been investigated when first occurs on the continuum of cognitive impairment for the purpose of early diagnoses. Alzheimer's disease (AD) is a neurodegenerative pathology that develops years prior to clinical manifestations and typically preceded by prodromal stages such as Mild Cognitive Impairment (MCI). Currently, the manual diagnostic procedures of both types are time consuming, following certain clinical criteria and neuropsychological examinations. Our study aims to establish state-of-the-art performance in the automatic identification of different dementia etiologies, including AD, MCI, and Possible AD (PoAD), and to determine whether patients with initial cognitive declines exhibit language deficits through the analysis of language samples deduced with the cookie theft picture description task. Data was derived from the cookie theft picture corpus of DementiaBank, from which all language samples of the identified etiologies were used, with a random subsampling technique that handles the skewness of the classes. Several original lexical and syntactic (i.e., lexicosyntactic) features were introduced and used alongside previously established lexicosyntactics to train machine learning (ML) classifiers against these etiologies. Further, a statistical analysis was conducted to uncover the deficiency across these etiologies. Our models resulted in benchmarks for differentiating all the identified classes with accuracies ranging between 95 to 98% and corresponding F1 values falling between 94 and 98%. The statistical analysis of our lexicosyntactic biomarkers shows that linguistic deviations are associated with prodromal as well as advanced neurodegenerative pathologies, being greatly impacted as cognitive decline increases and suggesting that language biomarkers may aid the early diagnosis of these pathologies.
Highlights
AND MOTIVATIONThe rising elderly population is a prominent demographic attribute of developed countries [1], [2]
On the other hand, aligning with Orimaye et al [39], our experiments show the powerfulness of n-grams features, capturing the most informative linguistic deficits between Healthy Control (HC) and people with different dementia stages
With regards to Orimaye et al [37], while their study is concerned with classifying the Mild Cognitive Impairment (MCI) and HC groups, our study investigated the language deficiency of MCI patients against both HC and Alzheimer’s disease (AD) participants may shed light on the correlation between the language deficiency and progression of dementia
Summary
The rising elderly population is a prominent demographic attribute of developed countries [1], [2]. Early diagnosis is the only hope for people with, or likely to develop, dementia. A timely diagnosis of dementia is fundamental for decelerating its progression as well as allowing maximized benefits of pharmaceutical interven-. The associate editor coordinating the review of this manuscript and approving it for publication was Hao Ji. tions that can mitigate the side effects in certain types of dementia [4]–[6]. It may stabilize or even curtail the decline in some prodromal dementia cases [7]–[10]. Clinical examinations of dementia typically involve multiple diagnostic procedures, which may be highly stressful and costly a major cause of late diagnosis
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