阅读障碍的分类预测模型

Yih-Choung Yu, Khaknazar Shyntassov, Amanuel Zewge, L. Gabel
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引用次数: 0

摘要

阅读障碍是一种阅读障碍,影响儿童的语言拼写,尽管有足够的智力和教育机会。如果学习障碍得不到治疗,孩子可能会经历长期的社会和情感问题,这可能会影响他们未来生活各方面的成功。早期发现和干预将有助于缩小正常发育儿童和阅读障碍儿童在获得阅读技能方面的差距。我们已经证明了阅读障碍的动物模型,基于候选阅读障碍易感基因的遗传模型,以及具有特定阅读障碍的儿童在虚拟Hebb-Williams迷宫任务中表现出共同的缺陷。由于虚拟迷宫任务不需要口头报告(快速进入语音处理)或依赖文本,因此表现不受组间阅读经验潜在差异的影响。虽然阅读障碍与虚拟Hebb-Williams迷宫任务表现之间的相关性已经得到证实,但通过实时观察非典型参与者(即阅读障碍参与者)在虚拟Hebb-Williams迷宫任务中的表现来分类目前尚不可行。一个基于机器学习算法的计算模型,可以根据迷宫学习的表现来预测阅读能力,它将以阅读风险百分比的形式实现实时反馈。本文介绍了采用基于机器学习的计算模型对虚拟迷宫性能进行分类的初步结果。分析了227名8-14岁学龄儿童的阅读数据和迷宫学习结果。将年龄和生理性别等多个变量应用到机器学习算法中,预测准确率达到70%以上。这一预测模型的成功开发将允许早期发现阅读障碍的风险,这可以导致早期干预,以缩小正常发育和阅读障碍儿童在获得阅读技能方面的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification Predictive Modeling of Dyslexia
Dyslexia is a reading disability that affects children across language orthographies, despite adequate intelligence and educational opportunity. If learning disabilities remain untreated, a child may experience long-term social and emotional problems, which may influence future success in all aspects of their lives. Early detection and intervention will help to close the gap between typically developing and reading impaired children in acquiring reading skills. We have demonstrated that animal models of dyslexia, genetic models based on candidate dyslexia susceptibility genes, and children with specific reading impairment show a common deficit on a virtual Hebb-Williams maze task. Since virtual maze task does not require oral reporting (rapid access to phonological processing) or rely on text, performance is not influenced by a potential difference in reading experience between groups. Although the correlation between dyslexia and the performance in the virtual Hebb-Williams maze task has been demonstrated, classification of atypical participants (i.e., dyslexic participants) through real-time observation of their performance on the virtual Hebb-Williams maze task is not feasible at this time. A computational model based on machine learning algorithms, that can predict reading ability based on maze learning performance, would enable real-time feedback of the performance in the form of at-risk percentages for reading. This paper presents the preliminary results of employing machine-learning based computational models to classify virtual maze performance on this task. Reading data and maze learning outcomes were analyzed from 227 school-aged children (8–14 years of age). Applying multiple variables, such as age and biological sex, into machine learning algorithms resulted in the prediction accuracy above 70%. Successful development of this predictive model would allow for early detection of risk for reading impairment, which can lead to early interventions to close the gap between typically developing and reading impaired children in acquiring reading skills.
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