How linguistic patterns and machine learning could detect early stages of dementia
Identifying the early symptoms of Alzheimer’s disease could make it possible to curb the decline in neural functioning associated with cognitive deterioration.
As published in IEEE Access, the use of linguistic patterns and machine learning models are one way a team at the Queensland University of Technology were able to detect early signs of dementia.
“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,” researchers stated in their findings.
“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.”
Upon analyzing more than 200 different language samples from the database DementiaBank, among patients with Alzheimer’s and mild cognitive impairment, along with healthy individuals, distinct linguistic patterns were established.
“We found people with dementia leaned towards using fewer nouns but more verbs, pronouns, and adjectives as dementia progressed compared to healthy adults,” said co-author Ahmed Alkenani in a news release.
“This is interesting, as previous research has revealed that nouns and verbs are learnt and activated in different brain regions which could be matched with the area of the brain that is first affected by dementia and help early intervention,” Alkenani added.
“Our ultimate aim is to develop a conversational agent or chatbot that could be used remotely to facilitate the initial diagnosis of early stage dementia as an attempt to replace traditional screening tests.”