Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity
Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity
复制标题
使用眼睛注视指标预测 ADHD 索引工作记忆容量
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Jayarathna
中科院分区：
文献类型：
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作者：
Anne M. P. Michalek;Gavindya Jayawardena;S. Jayarathna
ADHD被认为是一种持续到成年的诊断，影响教育和经济成果。越来越需要通过制定反映核心诊断标准的可靠和有效的结局指标来准确诊断这一人群。例如，患有ADHD的成年人与同龄人相比，工作记忆容量（WMC）减少。WMC的减少表明注意力控制缺陷，这与用于诊断ADHD的行为检查表上列出的许多症状一致。使用机器学习等计算方法来生成ADHD与WMC测量之间的关系，将有助于促进我们对成人ADHD的理解和治疗。本章将概述一项可行性研究，在该研究中，眼动追踪用于测量患有和不患有ADHD的成年人在WMC任务期间的眼睛注视指标，并应用机器学习算法来生成ADHD诊断所特有的特征集。本章将总结本研究的目的、方法、结果和影响。
ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.
诊断儿童注意力缺陷/多动障碍。
DOI：
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发表时间：
1998
期刊：
The Journal of clinical psychiatry.
影响因子：
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作者：
Greenhill,LL
通讯作者：
Greenhill,LL
影响因子：
3.7
作者：
Krejtz K;Duchowski AT;Niedzielska A;Biele C;Krejtz I
通讯作者：
Krejtz I
影响因子：
4.1
作者：
Engle, RW;Tuholski, SW;Conway, ARA
通讯作者：
Conway, ARA