Screening dyslexia for English using HCI measures and machine learning

More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that predict dyslexia by observing how people interact with a linguistic computer-based game. We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that people with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes. . Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features.Peer ReviewedPostprint (author's final draft

Recognizing Psychiatric Comorbidity With Reading Disorders

Reading disorder (RD), a specific learning disorder (SLD) of reading that includes impairment in word reading, reading fluency, and/or reading comprehension, is common in the general population but often is not comprehensively understood or assessed in mental health settings. In education settings, comorbid mental and associated disorders may be inadequately integrated into intervention plans. Assessment and intervention for RD may be delayed or absent in children with frequently co-occurring mental disorders not fully responding to treatment in both school and mental health settings. To address this oversight, this review summarizes current knowledge regarding RDs and common comorbid or co-occurring disorders that are important for mental health and school settings. We chose to highlight RD because it is the most common SLD, and connections to other often comorbid disorders have been more thoroughly described in the literature. Much of the literature we describe is on decoding-based RD (or developmental dyslexia) as it is the most common form of RD. In addition to risk for academic struggle and social, emotional, and behavioral problems, those with RD often show early evidence of combined or intertwined Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition childhood disorders. These include attention deficit hyperactivity disorder, anxiety and depression, disruptive, impulse-control, and conduct disorders, autism spectrum disorders, and other SLDs. The present review highlights issues and areas of controversy within these comorbidities, as well as directions for future research. An interdisciplinary, integrated approach between mental health professionals and educators can lead to comprehensive and targeted treatments encompassing both academic and mental health interventions. Such targeted treatments may contribute to improved educational and health-related outcomes in vulnerable youth. While there is a growing research literature on this association, more studies are needed of when to intervene and of the early and long-term benefits of comprehensive intervention

Screening dyslexia for English using HCI measures and machine learning

More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that predict dyslexia by observing how people interact with a linguistic computer-based game. We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that people with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes. . Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features.Peer Reviewe