Automatic detection of linguistic indicators as a means of early detection of Alzheimer's disease and of related dementias: A computational linguistics analysis
Abstract
In the present study, we analyzed written samples obtained from Greek native speakers diagnosed with Alzheimer's in mild and moderate stages and from age-matched cognitively normal controls (NC). We adopted a computational approach for the comparison of morpho-syntactic complexity and lexical variety in the samples. We used text classification approaches to assign the samples to one of the two groups. The classifiers were tested using various features: morpho-syntactic and lexical characteristics. The proposed method excels in discerning AD patients in mild and moderate stages from NC leading to the in-depth understanding of language deficits.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Other reports based on language and speech features ranged from 70% to 89.6% accuracy.
“Other reports based on language and speech features ranged from 70% to 89.6% accuracy.”
From Automatic classification of AD pathology in FTD phenotypes using natural speech (2024) · cites this paper as [18]
Cites
Automated classification of primary progressive aphasia subtypes from narrative speech transcripts
Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014) · Cortex · doi:10.1016/j.cortex.2012.12.006 · Cited by 229 · see citing works
Microlinguistic aspects of the oral narrative in patients with Alzheimer's disease
de Lira, J. O., Ortiz, K. Z., Campanha, A. C., Bertolucci, P. H. F., & Minett, T. S. C. (2011) · International Psychogeriatrics · doi:10.1017/s1041610210001092 · Cited by 137 · see citing works
Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse
Garrard, P., Rentoumi, V., Gesierich, B., Miller, B., & Gorno-Tempini, M. L. (2014) · Cortex · doi:10.1016/j.cortex.2013.05.008 · Cited by 87 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Speech- and language-based classification of Alzheimer's disease: a systematic review
Vigo, I., Coelho, L., & Reis, S. (2022) · Bioengineering · doi:10.3390/bioengineering9010027 · Cited by 83 · see citing works
Lexical and Acoustic Speech Features Relating to Alzheimer Disease Pathology
Cho, S., Cousins, K. A. Q., Shellikeri, S., Ash, S., Irwin, D. J., Liberman, M. Y., Grossman, M., & Nevler, N. (2022) · Neurology · doi:10.1212/wnl.0000000000200581 · Cited by 51 · see citing works
Automatic classification of AD pathology in FTD phenotypes using natural speech
Cho, S., Olm, C. A., Ash, S., Shellikeri, S., Agmon, G., Cousins, K. A. Q., Irwin, D. J., Grossman, M., Liberman, M., & Nevler, N. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13748 · Cited by 13 · see citing works
Speech evaluation of patients with alzheimer’s disease using an automatic interviewer
Alonso Hernández, J. B., Barragán Pulido, M. L., Gil Bordón, J. M., Ferrer Ballester, M. Á., & Travieso González, C. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116386 · Cited by 5 · see citing works
de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200888 · Cited by 5 · see citing works
- Authors
- Rentoumi, V., Paliouras, G., Danasi, E., Arfani, D., Fragkopoulou, K., Varlokosta, S., & Papadatos, S.
- Affiliations
- National Centre of Scientific Research "Demokritos" (GR)
- National and Kapodistrian University of Athens (GR)
- Technological Educational Institute of Central Greece (GR)
- Venue
- 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 000033-000038
- OpenAlex
- W2787145274
- Cited by
- 39
- References
- 3
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 6 · 2022: 5 · 2021: 7 · 2020: 7 · 2019: 5 · 2018: 2
- FWCI
- 11.09
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Cognitive Infocommunications (publishedVersion)
- Funders
- Horizon 2020 Framework Programme
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- Alzheimer's disease, early detection, dementia, linguistic indicators, morphosyntactic complexity, lexical diversity, text classification, language deficits, Greek speakers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
