Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia
Open accessAbstract
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.
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In Alzheimer's disease, ASLA markers differentiate patients from healthy persons and other patient groups, predict overall cognitive status, outperform certain cognitive tests in predicting dementia onset, and correlate with volume of the hippocampus and other core atrophy regions.
“In Alzheimer's disease, they differentiate patients from healthy persons 13,58-62 and other patient groups, 63 predict overall cognitive status, 64 outperform certain cognitive tests in predicting dementia onset, 65 and correlate with volume of the hippocampus and other core atrophy regions. 66,67”
From Speech and language markers of neurodegeneration: a call for global equity (2023) · cites this paper as [58]
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- Authors
- Orimaye, S. O., Wong, J. S.-M., & Wong, C. P.
- Affiliations
- East Tennessee State University (US)
- Royal College of Surgeons in Ireland (IE)
- Corresponding author
- Orimaye, Sylvester Olubolu
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-11-07
- Volume
- 13
- Issue
- 11
- Pages
- e0205636
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6221274
- OpenAlex
- W4233250549
- Cited by
- 52
- References
- 35
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 4 · 2023: 11 · 2022: 5 · 2021: 14 · 2020: 6 · 2019: 3
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-03-14)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons - East Tennessee State University (East Tennessee State University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- mild cognitive impairment, Alzheimer's disease, Alzheimer-type dementia, automated diagnosis, verbal utterances, linguistic biomarkers, n-grams, DementiaBank, dementia classification
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