Cited by 87Dementia and Cognitive Impairment ResearchNeurobiology of Language and BilingualismArtificial Intelligence in Healthcare

Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse

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Garrard, Peter, Rentoumi, Vassiliki, Gesierich, Benno, Miller, Bruce, Gorno-Tempini, Maria Luisa·2014·Cortex

Abstract

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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Research works printed but not linked: 3

  1. Joachims T Text caegorisation with support vector machines: Learning with many relevant features 1998 137 142 European Conference on Machine Learning
  2. Mitchell T Machine Learning 1997 McGraw-Hill
  3. Mummery CJ Patterson K Price CJ Ashburner J Frackowiak RS Hodges JR A voxel-based morphometry study of semantic dementia: Relationship between temporal lobe atrophy and semantic memory Annals of Neurology 2000 47 36 45 10632099

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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)
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
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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