Probabilistic graphical models of dyslexia

Yair Lakretz, Gal Chechik, Naama Friedmann, Michal Rosen-Zvi

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)

Abstract

Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading errors made by dyslexic people: an LDA-based model and two Naïve Bayes models which differ by their assumptions about the generation process of reading errors. The models are trained on a large corpus of reading errors. Results show that a Naïve Bayes model achieves highest accuracy compared to labels given by clinicians (AUC = 0.801 ± 0.05), thus providing the first automated and objective diagnosis tool for dyslexia which is solely based on reading errors data. Results also show that the LDA-based model best captures patterns of reading errors and could therefore contribute to the understanding of dyslexia and to future improvement of the diagnostic procedure. Finally, we draw on our results to shed light on a theoretical debate about the definition and heterogeneity of dyslexia. Our results support a model assuming multiple dyslexia subtypes, that of a heterogeneous view of dyslexia.

Original languageEnglish
Title of host publicationKDD '15
Subtitle of host publicationProceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages1919-1928
Number of pages10
Volume2015-August
ISBN (Electronic)9781450336642
DOIs
Publication statusPublished - 10 Aug 2015
Externally publishedYes
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: 10 Aug 201513 Aug 2015

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period10/08/1513/08/15

Keywords

  • Diagnosis
  • Dyslexia
  • Latent dirichlet allocation
  • Naïve Bayes
  • Probabilistic graphical models

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