These findings show a high prevalence of silent liver disease with advanced fibrosis mainly related to NAFLD in adult European subjects without known liver disease. An LS value less than 9.2 kPa predicts the absence of significant liver fibrosis with high accuracy and could be used for screening purposes.
In this article we examine language processing and development in Catalan or Spanish-speaking children with SLI, focusing on the study of the verb. We analyse the key initial phase of its process of acquisition and aim to define common features of the SLI group that distinguish them from children with normal language development. We intend to identify more precisely the kind of delay shown by these children in a language with a rich verb morphology, in terms of both structure and chronology. The sample comprised 18 Catalan-Spanish bilingual pre-school children, assigned to three groups of six; an SLI group and two control groups, one matched for age and the other matched for MLU-w. Developmental data were obtained by recording situations of spontaneous speech at two different time points. Certain differences were found between groups in verb production. Production of verb inflection by children with SLI was only partial at the first evaluation; they maintained the same percentage of errors after a year. The patterns of correct and incorrect verb forms found in Catalan and Spanish do not corroborate the EOI hypothesis, but they support the Surface Hypothesis, given that the number of errors is not particularly high. This suggests the presence of limitations in subjects' processing ability, linked to the typological characteristics of the specific language being learnt.
Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -a tablet instead of a desktop computer- reaching a recall of over 78% for the class with dyslexia for children 12 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool in Spanish based on our methods has already been used by more than 200,000 people.
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 scalable early detection via machine learning models that predict reading and writing difficulties by watching how people interact with a linguistic web-based game: Dytective. The design of Dytective is based on (i) the empirical linguistic analysis of the errors that people with dyslexia make, (ii) principles of language acquisition, and (iii) specific linguistic skills related to dyslexia. Experiments with 243 children and adults (95 with diagnosed dyslexia) revealed differences in how people with dyslexia read and write. We trained a machine learning model that was able to predict dyslexia with 83% accuracy in a held-out test set with 100 participants. Currently, we are working with schools to put our approach into practice at scale to reduce school failure as a primary way dyslexia is diagnosed.
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