Enhancing the classification of aphasia: a statistical analysis using connected speech. Issue 12 (2nd December 2022)

• Record Type: Journal Article
• Authors: Fromm, Davida; Greenhouse, Joel; Pudil, Mitchell; Shi, Yichun; MacWhinney, Brian
• Is Part Of: Aphasiology. Volume 36:Issue 12(2022)
• Page Start: 1492
• Page End: 1519
• Publication Date: 2022-12-02
• Subjects: Aphasia -- discourse -- clustering methods -- classification; Aphasia -- Periodicals; Aphasia; 616.8552
• DOI: 10.1080/02687038.2021.1975636
• Languages: English
• ISSNs: 0268-7038

Abstract

ABSTRACT: Background: 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). Aims: 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. Methods & Procedures: 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. Outcomes & Results: 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. Conclusion: 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.