Details of talk
|Title||Automated Classification of Post-Stroke Aphasia by Severity|
|Presenter||Tea Kristiane Espeland Uggen (University of Technology, Sydney)|
|Author(s)||Tea Kristiane Espeland Uggen, Tapan Rai and Erin Godecke|
Aphasia is a post-stroke communication impairment identified in approximately one third of stroke survivors. Assessing the level of severity for patients with aphasia is necessary in order to determine the optimal rehabilitation pathway for each patient. This paper aims to predict aphasia severity through natural language processing and machine learning techniques. Predictive analysis was conducted in this paper based on 51 patient transcripts from English-speaking stroke survivors with aphasia. Numerical linguistic measures based on sentence structure, syntax and characteristics of normal speech were derived from these transcripts to perform predictive modelling using Support Vector Machines (SVM). Two different SVM models were conducted to predict aphasia severity. The results of model 1 show that these linguistic measures are able to predict aphasia severity (mild, moderate, severe) at approximately a 72.6\% accuracy. In model 2, the relevance of patientsí speech to the task was incorporated to account for patients who deviate from the topic of the task. This addition further improved the accuracy by 2.6\% from model 1 to an accuracy of approximately 74.5\%. Further research involves determining which measures of speech are most relevant in predicting aphasia severity as well as obtaining other appropriate measures to be included in the predictive analysis to increase the accuracy of the models.
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