Vector-space topic models for detecting Alzheimer's disease
Open accessAbstract
Semantic deficit is a symptom of language impairment in Alzheimer's disease (AD).We present a generalizable method for automatic generation of information content units (ICUs) for a picture used in a standard clinical task, achieving high recall, 96.8%, of human-supplied ICUs.We use the automatically generated topic model to extract semantic features, and train a random forest classifier to achieve an F-score of 0.74 in binary classification of controls versus people with AD using a set of only 12 features.This is comparable to results (0.72 F-score) with a set of 85 manual features.Adding semantic information to a set of standard lexicosyntactic and acoustic features improves F-score to 0.80.While control and dementia subjects discuss the same topics in the same contexts, controls are more informative per second of speech.
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Garrard et al. achieved over 90% classification accuracy for distinguishing semantic dementia from healthy controls and for classifying left- versus right-predominant SD.
“Classification accuracy of over 90% was reached for categorizing the participants for two tasks: (1) participants with SD against the healthy control participants, and (2) classifying the left- and right-predominant variants of SD.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [4]
First-order lexeme-level features are useful biomarkers for detecting the presence or predicting the onset of MCI, AD, CTE, and potentially other conditions.
“It is clear from the literature that first-order lexeme-level features, i.e. those related to lexical diversity and density, are useful biomarkers for detecting the presence or predicting the onset of a variety of conditions, such as MCI, AD, CTE, and potentially several others.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [4]
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Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155 · see citing works
The CHILDES project: tools for analyzing talk
Pye, C., & MacWhinney, B. (1992) · Language · doi:10.2307/416745 · Cited by 5,229 · see citing works
The CHILDES project: tools for analyzing talk
MacWhinney, B. (1992) · Child Language Teaching and Therapy · doi:10.1177/026565909200800211 · Cited by 3,421 · see citing works
The assessment of aphasia and related disorders
Goodglass, H., & Kaplan, E. (1972) · Medical Entomology and Zoology · Cited by 3,258 · see citing works
Hécaen, H. (1983) · Neuropsychologia · doi:10.1016/0028-3932(83)90089-1 · Cited by 1,700 · see citing works
Linguistic Features Identify Alzheimer’s Disease in Narrative Speech
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150520 · Cited by 797 · see citing works
Self-citationSnowdon, D. A. (1996) · JAMA · doi:10.1001/jama.1996.03530310034029 · Cited by 696 · see citing works
(2012) · SpringerReference · doi:10.1007/springerreference_183701 · Cited by 595 · see citing works
Communication and Cognition in Normal Aging and Dementia
Bayles, K. A., Kaszniak, A. W., & Tomoeda, C. K. (1987) · Medical Entomology and Zoology · Cited by 398 · see citing works
Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease
Ahmed, S., Haigh, A.-M. F., de Jager, C. A., & Garrard, P. (2013) · Brain · doi:10.1093/brain/awt269 · Cited by 362 · see citing works
Language disintegration in dementia: Effects of etiology and severity
Hier, D. B., Hagenlocker, K., & Shindler, A. G. (1985) · Brain and Language · doi:10.1016/0093-934x(85)90124-5 · Cited by 279 · see citing works
The assessment of aphasîa and related disorders
Assal, G. (1973) · Neuropsychologia · doi:10.1016/0028-3932(73)90075-4 · Cited by 275 · see citing works
Forbes-McKay, K. E., & Venneri, A. (2005) · Neurological Sciences · doi:10.1007/s10072-005-0467-9 · Cited by 261 · see citing works
Le, X., Lancashire, I., Hirst, G., & Jokel, R. (2011) · Literary and Linguistic Computing · doi:10.1093/llc/fqr013 · Cited by 229 · see citing works
Comparative Study of Oral and Written Picture Description in Patients with Alzheimer's Disease
Croisile, B., Ska, B., Brabant, M.-J., Duchêne, A., Lepage, Y., Aimard, G., & Trillet, M. (1996) · Brain and Language · doi:10.1006/brln.1996.0033 · Cited by 216 · see citing works
Giles, E., Patterson, K., & Hodges, J. R. (1996) · Aphasiology · doi:10.1080/02687039608248419 · Cited by 139 · see citing works
Communication and Cognition in Normal Aging and Dementia
Newton, M. (1987) · Journal of Gerontology · doi:10.1093/geronj/42.5.571 · Cited by 99 · see citing works
Ahmed, S., de Jager, C. A., Haigh, A.-M., & Garrard, P. (2013) · Neuropsychology · doi:10.1037/a0031288 · Cited by 90 · see citing works
Pakhomov, S. V. S., Smith, G. E., Chacon, D., Feliciano, Y., Graff-Radford, N., Caselli, R., & Knopman, D. S. (2010) · Cognitive and Behavioral Neurology · doi:10.1097/wnn.0b013e3181c5dde3 · Cited by 64 · see citing works
The natural history of Alzheimer’s disease
Corey‐Bloom, J., & Rafii, M. S. (2017) · doi:10.1201/9781315381572-56 · Cited by 39 · see citing works
Lai, Y., Pai, H., & Lin, Y. (2009) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2009.03.004 · Cited by 35 · see citing works
Speech-based automated cognitive status assessment
Hakkani-Tür, D. Z., Vergyri, D., & Tür, G. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-105 · Cited by 27 · see citing works
Detecting semantic changes in Alzheimer’s disease with vector space models
Fraser, K., & Hirst, G. (2016) · Language Resources and Evaluation · Cited by 14 · see citing works
The Natural History of Alzheimer’s Disease
López, O. L., & Becker, J. T. (2004) · doi:10.1093/oso/9780198508304.003.0004 · Cited by 6 · see citing works
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Research works printed but not linked: 1
- C. Ballard, S. Gauthier, A. Corbett, C. Brayne, D. Aars- land, and E. Jones. 2011. Alzheimer's disease. The Lancet, 377(9770):1019-1031.
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Alzheimer’s Dementia Recognition Through Spontaneous Speech: The ADReSS Challenge
Luz, S., Haider, F., Fuente, S. D. L., Fromm, D., & MacWhinney, B. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2571 · Cited by 274 · see citing works
A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders
Voleti, R., Liss, J. M., & Berisha, V. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2952087 · Cited by 96 · see citing works
Lindsay, H., Tröger, J., & König, A. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.642033 · Cited by 64 · see citing works
Martinc, M., Haider, F., Pollak, S., & Luz, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.642647 · Cited by 55 · see citing works
“Therefore, this dataset has been used in several studies, including the studies by Fraser et al. (2016), Hernández-Domínguez et al. (2018), and others (Yancheva and Rudzicz, 2016; Luz, 2017; Orimaye et al., 2017; Guo et al., 2019; Mirheidari et al., 2019b; Haider et al., 2020).”
MethodTalk2Me: Automated linguistic data collection for personal assessment
Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23 · see citing works
Self-citation
- Authors
- Yancheva, M., & Rudzicz, F.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Yancheva, Maria
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 2337-2346
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2514692010
- Cited by
- 57
- References
- 25
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 4 · 2023: 5 · 2022: 2 · 2021: 10 · 2020: 13 · 2019: 7 · 2018: 5 · 2017: 2
- FWCI
- 15.96
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Toronto Rehabilitation Institute
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- Alzheimer's disease detection, semantic deficits, topic modeling, picture description, semantic features, random forest classification, speech analysis, dementia diagnosis, acoustic features
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