title: Individualized Early Prediction of Familial Risk of Dyslexia: A Study of Infant Vocabulary Development.
abstract: We examined early vocabulary development in children at familial risk (FR) of dyslexia and typically developing (TD) children between 17 and 35 months of age. We trained a support vector machine to classify TD and FR using these vocabulary data at theindividuallevel. The Dutch version of the McArthur-Bates Communicative Development Inventory (Words and Sentences) (N-CDI) was used to measure vocabulary development. We analyzed group-level differences for both total vocabulary as well as lexical classes: common nouns, predicates, and closed class words. The generalizability of the classification model was tested using cross-validation. At the group level, for both total vocabulary and the composites, the difference between TD and FR was most pronounced at 19-20 months, with FRs having lower scores. For the individual prediction, highest cross-validation accuracy (68%) was obtained at 19-20 months, with sensitivity (correctly classified FR) being 70% and specificity (correctly classified TD) being 67%. There is a sensitive window in which the difference between FR and TD is most evident. Machine learning methods are promising techniques for separating FR and TD children at an early age, before they start reading.
authors.affiliation: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center UtrechtUtrecht, Netherlands; Utrecht Institute of Linguistics OTS, Utrecht UniversityUtrecht, Netherlands.
authors.forename: Ao
authors.initials: A
authors.lastname: Chen
authors.affiliation: Utrecht Institute of Linguistics OTS, Utrecht University Utrecht, Netherlands.
authors.forename: Frank
authors.initials: F
authors.lastname: Wijnen
authors.affiliation: Center for Language and Cognition Groningen, University of Groningen Groningen, Netherlands.
authors.forename: Charlotte
authors.initials: C
authors.lastname: Koster
authors.affiliation: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht Utrecht, Netherlands.
authors.forename: Hugo
authors.initials: H
authors.lastname: Schnack
keywords: developmental trajectories; dyslexia; machine learning; predictions; vocabulary acquisition