Discriminating Pattern Mining for Diagnosing Reading Disorders

Abstract

Tachistoscopes are devices that display a word for several seconds and ask the user to write down the word. They have been widely employed to increase recognition speed, to increase reading comprehension and, especially, to individuate reading difficulties and disabilities. Once the therapist is provided with the answers of the patients, a challenging problem is the analysis of the strings to individuate common patterns in the erroneous strings that could raise suspicion of related disabilities. In this direction, this work presents a machine learning technique aimed at mining exceptional string patterns and is precisely designed to tackle the above-mentioned problem. The technique is based on non-negative matrix factorization, nnmf, and exploits as features the structure of the words in terms of the letters composing them. To the best of our knowledge, this is the first attempt of mining tachistoscope answers to discover intrinsic peculiarities of the words possibly involved in reading disabilities. From the technical point of view, we present a novel variant of nnmf methods with the adjunctive goal of discriminating between sets. The technique has been experimented in a real case study with the help of an Italian speech therapist center that collaborate with this work.