Litcius/Paper detail

Determinants of aphasia recovery: exploratory decision tree analysis

Durjoy Lahiri, Souvik Dubey, Alfredo Ardila, Debasish Sanyal, Biman Kanti Ray

2020Language Cognition and Neuroscience16 citationsDOI

Abstract

One hundred and sixty-three aphasia patients underwent initial language examination during the first week following stroke and 90–100 days post-stroke. Demographic factors (age, gender, and number of years of formal education), lesion-related factors (type of stroke, lesion volume, cortical versus sub-cortical location, and site of lesion), as well as initial severity and type of aphasia were taken as independent variables while aphasia recovery (in terms of no change versus change to a milder type or complete recovery) was the dependent variable. Chi square automatic interaction detection (CHAID) was performed to assess predictor importance and formulate a predictive model for aphasia recovery. Initial severity of aphasia followed by initial aphasia symptomatology was found to be the most important factor determining aphasia recovery. Age and gender had some importance. Lesion-related factors did not reach statistical significance as independent determinant of aphasia recovery. The predictive value of the model was 66.87%

Topics & Concepts

AphasiaStroke (engine)CHAIDPsychologyLesionStroke recoveryPredictive valueRehabilitationAudiologyDecision treeMedicineCognitive psychologyPsychiatryComputer scienceArtificial intelligenceNeuroscienceInternal medicineEngineeringMechanical engineeringNeurobiology of Language and BilingualismCerebrovascular and Carotid Artery DiseasesAcute Ischemic Stroke Management