Use of machine learning for behavioral distinction of autism and ADHD.
Use of machine learning for behavioral distinction of autism and ADHD.
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DOI：
10.1038/tp.2015.221
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发表时间：
2016-02-09
影响因子：
6.8
通讯作者：
Wall DP
中科院分区：
文献类型：
--
作者：
Duda M;Ma R;Haber N;Wall DP
尽管自闭症谱系障碍（ASD）和注意缺陷多动障碍（ADHD）的患病率继续上升，共同影响当今超过10%的儿科人群，但诊断方法仍然主观、繁琐且耗时。在最初的怀疑和诊断之间有一年以上的差距，可以应用治疗和行为干预的宝贵时间被浪费了，因为这些疾病仍未被发现。快速准确地评估这些和其他发育障碍风险的方法对于简化诊断过程并为家庭更快地获得急需的治疗是必要的。使用正向特征选择，以及欠采样和10倍交叉验证，我们在来自2925名ASD（n=2775）或ADHD（n=150）患者的完整65项社会反应量表评分表上训练和测试了6个机器学习模型。我们发现，通过这种筛选工具测量的65种行为中的5种足以以高准确度（曲线下面积=0.965）区分ASD和ADHD。这些结果支持了以下假设：（1）机器学习可以用于以高精度区分自闭症和ADHD，（2）这种区分可以使用少量常用的测量行为来进行。我们的研究结果显示，作为一种电子管理的，由医生指导的资源，用于初步风险评估和/或临床前筛查和分诊，这可能有助于加快这些疾病的诊断。
Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today's pediatric population, the methods of diagnosis remain subjective, cumbersome and time intensive. With gaps upward of a year between initial suspicion and diagnosis, valuable time where treatments and behavioral interventions could be applied is lost as these disorders remain undetected. Methods to quickly and accurately assess risk for these, and other, developmental disorders are necessary to streamline the process of diagnosis and provide families access to much-needed therapies sooner. Using forward feature selection, as well as undersampling and 10-fold cross-validation, we trained and tested six machine learning models on complete 65-item Social Responsiveness Scale score sheets from 2925 individuals with either ASD (n=2775) or ADHD (n=150). We found that five of the 65 behaviors measured by this screening tool were sufficient to distinguish ASD from ADHD with high accuracy (area under the curve=0.965). These results support the hypotheses that (1) machine learning can be used to discern between autism and ADHD with high accuracy and (2) this distinction can be made using a small number of commonly measured behaviors. Our findings show promise for use as an electronically administered, caregiver-directed resource for preliminary risk evaluation and/or pre-clinical screening and triage that could help to speed the diagnosis of these disorders.