Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse
Open accessAbstract
Advances in automatic text classification have been necessitated by the rapid increase in the availability of digital documents. Machine learning (ML) algorithms can 'learn' from data: for instance a ML system can be trained on a set of features derived from written texts belonging to known categories, and learn to distinguish between them. Such a trained system can then be used to classify unseen texts. In this paper, we explore the potential of the technique to classify transcribed speech samples along clinical dimensions, using vocabulary data alone. We report the accuracy with which two related ML algorithms [naive Bayes Gaussian (NBG) and naive Bayes multinomial (NBM)] categorized picture descriptions produced by: 32 semantic dementia (SD) patients versus 10 healthy, age-matched controls; and SD patients with left- (n = 21) versus right-predominant (n = 11) patterns of temporal lobe atrophy. We used information gain (IG) to identify the vocabulary features that were most informative to each of these two distinctions. In the SD versus control classification task, both algorithms achieved accuracies of greater than 90%. In the right- versus left-temporal lobe predominant classification, NBM achieved a high level of accuracy (88%), but this was achieved by both NBM and NBG when the features used in the training set were restricted to those with high values of IG. The most informative features for the patient versus control task were low frequency content words, generic terms and components of metanarrative statements. For the right versus left task the number of informative lexical features was too small to support any specific inferences. An enriched feature set, including values derived from Quantitative Production Analysis (QPA) may shed further light on this little understood distinction.
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One prior study demonstrated the potential of automated connected speech analysis in distinguishing between rATL and lATL atrophy.
“Notably, 1 prior study demonstrated the potential of automated connected speech analysis in distinguishing between rATL and lATL atrophy.17”
From Automated Speech Analysis to Differentiate Frontal and Right Anterior Temporal Lobe Atrophy in Frontotemporal Dementia (2025) · cites this paper as [17]
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- Authors
- Garrard, P., Rentoumi, V., Gesierich, B., Miller, B., & Gorno-Tempini, M. L.
- Affiliations
- St George's, University of London (GB)
- University of California, San Francisco (US)
- Corresponding author
- Garrard, Peter
- Venue
- Cortex
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 55
- Pages
- 122-129
- ISSN
- 0010-9452 (ISSN-L)
- PMC
- PMC4072460
- OpenAlex
- W1974455934
- Cited by
- 87
- References
- 41
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 7 · 2022: 6 · 2021: 10 · 2020: 13 · 2019: 9 · 2018: 7 · 2017: 12 · 2016: 4 · 2015: 5 · 2014: 4
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cortex (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Iris (University of Trento) (submittedVersion)
- Funders
- National Institute on Aging (P01AG019724)
- National Institute of Neurological Disorders and Stroke (R01NS050915)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- semantic dementia, discourse analysis, speech classification, temporal lobe atrophy, laterality, picture description, lexical features, information gain, Naive Bayes classifier, metanarrative, low-frequency words, neuropsychological diagnosis
- MeSH terms
- Aged, Artificial Intelligence, Atrophy, Bayes Theorem, Female, Humans, Functional Laterality, Magnetic Resonance Imaging, Male, Middle Aged, Speech Disorders, Statistics as Topic, Temporal Lobe, Vocabulary, Case-Control Studies, Frontotemporal Dementia
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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.
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