Automated classification of primary progressive aphasia subtypes from narrative speech transcripts.
In the early stages of neurodegenerative disorders, individuals may exhibit a decline in language abilities that is difficult to quantify with standardized tests. Careful analysis of connected speech can provide valuable information about a patient's language capacities. To date, this type of analys...
| Publicado en: | Cortex: A Journal Devoted to the Study of the Nervous System & Behavior Vol. 55; pp. 43 - 61 |
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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=107794416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107794416 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: 107794416 107794416 2012598027 10.1016/j.cortex.2012.12.006 NLM23332818 107794416 ppf: 43 ppct: 18 formats: tig: atl: Automated classification of primary progressive aphasia subtypes from narrative speech transcripts. aug: au: Fraser, Kathleen C Meltzer, Jed A Graham, Naida L Leonard, Carol Hirst, Graeme Black, Sandra E Rochon, Elizabeth affil: Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. Electronic address: kfraser@cs.toronto.edu. sug: subj: Aphasia Diagnosis Artificial Intelligence Frontotemporal Dementia Diagnosis Semantics Speech Physiology Aged Probability Case Control Studies Computer Simulation Female Frontotemporal Dementia Physiopathology Human Language Male Middle Age Narratives Aphasia Physiopathology Algorithms Aged: 65+ years Middle Aged: 45-64 years Female Male ab: In the early stages of neurodegenerative disorders, individuals may exhibit a decline in language abilities that is difficult to quantify with standardized tests. Careful analysis of connected speech can provide valuable information about a patient's language capacities. To date, this type of analysis has been limited by its time-consuming nature. In this study, we present a method for evaluating and classifying connected speech in primary progressive aphasia using computational techniques. Syntactic and semantic features were automatically extracted from transcriptions of narrative speech for three groups: semantic dementia (SD), progressive nonfluent aphasia (PNFA), and healthy controls. Features that varied significantly between the groups were used to train machine learning classifiers, which were then tested on held-out data. We achieved accuracies well above baseline on the three binary classification tasks. An analysis of the influential features showed that in contrast with controls, both patient groups tended to use words which were higher in frequency (especially nouns for SD, and verbs for PNFA). The SD patients also tended to use words (especially nouns) that were higher in familiarity, and they produced fewer nouns, but more demonstratives and adverbs, than controls. The speech of the PNFA group tended to be slower and incorporate shorter words than controls. The patient groups were distinguished from each other by the SD patients' relatively increased use of words which are high in frequency and/or familiarity. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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