Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse.
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 categori...
| Publicado en: | Cortex: A Journal Devoted to the Study of the Nervous System & Behavior Vol. 55; pp. 122 - 130 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Masson SPA
2014 Jun
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=107794421&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107794421 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00109452 GMP jtl: Cortex: A Journal Devoted to the Study of the Nervous System & Behavior issn: 00109452 maglogo: N pubinfo: dt: 2014 Jun vid: 55 pid: 12473 pub: Masson SPA artinfo: ui: 107794421 107794421 NLM23876449 2012598033 10.1016/j.cortex.2013.05.008 NLM23876449 PMC4072460 107794421 ppf: 122 ppct: 8 formats: tig: atl: Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse. aug: au: Garrard, Peter Rentoumi, Vassiliki Gesierich, Benno Miller, Bruce Gorno-Tempini, Maria Luisa affil: Stroke and Dementia Research Centre, St George's, University of London, Cranmer Terrace, London SW17 ORE, UK. Electronic address: pgarrard@sgul.ac.uk. sug: subj: Artificial Intelligence Frontotemporal Dementia Diagnosis Dominance, Cerebral Speech Disorders Diagnosis Temporal Lobe Pathology Vocabulary Aged Atrophy Probability Case Control Studies Female Frontotemporal Dementia Pathology Frontotemporal Dementia Physiopathology Human Magnetic Resonance Imaging Male Middle Age Speech Disorders Pathology Speech Disorders Physiopathology Statistics Aged: 65+ years Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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