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Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse
Garrard P, Rentoumi V, Gesierich B, Miller B, Gorno-Tempini ML.. Cortex; a journal devoted to the study of the nervous system and behavior, 2013-06-14
Topic: Risk, prevention and care in population studies
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.
Key findings
- 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.
- 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.
Why it matters
Machine learning algorithms classified speech samples from 32 semantic dementia patients versus 10 healthy controls with over 90% accuracy, and distinguished left- versus right-predominant temporal lobe atrophy with up to 88% accuracy. This is about classifying a related dementia from language and does not test any treatment, cure or prevention for Alzheimer's disease.
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Study details
- Participants21
- Evidence strengthLow
How this study scored 44/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Cortex; a journal devoted to the study of the nervous system and behavior) |
| Journal's citation record | 2 / 4 | Cortex: journal h-index 202 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2014 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 21 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 88 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.3 citations a year over 14 year(s) since 2014 |
| Impact for its field and year | 2 / 6 | Cited 2.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 1 / 4 | Cited 10 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Maria Luisa Gorno‐Tempini: h-index 92 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 23876449) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 44 / 100 | — |
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Garrard, P., Rentoumi, V., Gesierich, B., Miller, B., & Gorno-Tempini, M. L. (2014). Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse. Cortex; a journal devoted to the study of the nervous system and behavior. https://doi.org/10.1016/j.cortex.2013.05.008
Vancouver
Garrard P, Rentoumi V, Gesierich B, Miller B, Gorno-Tempini ML. Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse. Cortex; a journal devoted to the study of the nervous system and behavior. 2014. doi:10.1016/j.cortex.2013.05.008. PMID: 23876449.
BibTeX
@article{garrard2014-23876449,
author = {Garrard, P. and Rentoumi, V. and Gesierich, B. and Miller, B. and Gorno-Tempini, M. L.},
title = {{Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse}},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
year = {2014},
doi = {10.1016/j.cortex.2013.05.008},
pmid = {23876449},
url = {https://doi.org/10.1016/j.cortex.2013.05.008}
}
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