Enhancing the classification of aphasia: a statistical analysis using connected speech.
Large-shared databases and automated language analyses allow for the application of new data analysis techniques that can shed new light on the connected speech of people with aphasia (PWA). To identify coherent clusters of PWA based on language output using unsupervised statistical algorithms and t...
| Published in: | Aphasiology Vol. 36; no. 12; pp. 1492 - 1520 |
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| Main Authors: | , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Taylor & Francis Ltd
Dec2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160003925&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160003925 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02687038 B6J jtl: Aphasiology issn: 02687038 maglogo: N pubinfo: dt: Dec2022 vid: 36 iid: 12 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 160003925 160003925 160003925 10.1080/02687038.2021.1975636 160003925 ppf: 1492 ppct: 28 formats: tig: atl: Enhancing the classification of aphasia: a statistical analysis using connected speech. aug: au: Fromm, Davida Greenhouse, Joel Pudil, Mitchell Shi, Yichun MacWhinney, Brian affil: Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA sug: subj: Aphasia Classification Algorithms Speech Human Storytelling Task Performance and Analysis Discourse Analysis Descriptive Statistics ab: Large-shared databases and automated language analyses allow for the application of new data analysis techniques that can shed new light on the connected speech of people with aphasia (PWA). To identify coherent clusters of PWA based on language output using unsupervised statistical algorithms and to identify features that are most strongly associated with those clusters. Clustering and classification methods were applied to language production data from 168 PWA. Language samples were from a standard discourse protocol tapping four genres: free speech personal narratives, picture descriptions, Cinderella storytelling, and procedural discourse. Seven distinct clusters of PWA were identified by the K-means algorithm. Using the random forest algorithm, a classification tree was proposed and validated, showing 91% agreement with the cluster assignments. This representative tree used only two variables to divide the data into distinct groups: total words from free speech tasks and total closed-class words from the Cinderella storytelling task. Connected speech data can be used to distinguish PWA into coherent groups, providing insight into traditional aphasia classifications, factors that may guide discourse research and clinical work. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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