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Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning

机译:通过基于特征选择的机器学习搜索用于自闭症检测的最小行为集

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摘要

Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in the United States remains close to 4—well past the developmental window when early intervention has the largest gains. This emphasizes the importance of developing accurate methods to detect risk faster than the current standards of care. In the present study, we used machine learning to evaluate one of the best and most widely used instruments for clinical assessment of ASD, the Autism Diagnostic Observation Schedule (ADOS) to test whether only a subset of behaviors can differentiate between children on and off the autism spectrum. ADOS relies on behavioral observation in a clinical setting and consists of four modules, with module 2 reserved for individuals with some vocabulary and module 3 for higher levels of cognitive functioning. We ran eight machine learning algorithms using stepwise backward feature selection on score sheets from modules 2 and 3 from 4540 individuals. We found that 9 of the 28 behaviors captured by items from module 2, and 12 of the 28 behaviors captured by module 3 are sufficient to detect ASD risk with 98.27% and 97.66% accuracy, respectively. A greater than 55% reduction in the number of behaviorals with negligible loss of accuracy across both modules suggests a role for computational and statistical methods to streamline ASD risk detection and screening. These results may help enable development of mobile and parent-directed methods for preliminary risk evaluation and/or clinical triage that reach a larger percentage of the population and help to lower the average age of detection and diagnosis.
机译:尽管自闭症谱系障碍(ASD)的患病率在过去几年中急剧上升,达到68分之一,但美国的平均诊断年龄仍接近4岁,远远超过了早期干预获得最大收益的发展期。这强调了开发准确的方法以比当前的护理标准更快地检测风险的重要性。在本研究中,我们使用机器学习来评估ASD临床评估的最佳和最广泛使用的工具之一,即自闭症诊断观察时间表(ADOS),以测试仅行为的一个子集是否可以区分儿童的行为。自闭症谱系。 ADOS依靠临床环境中的行为观察,由四个模块组成,其中模块2保留给具有一定词汇量的个人使用,模块3保留更高水平的认知功能。我们在来自4540个人的模块2和3的评分表上使用逐步向后的特征选择运行了八种机器学习算法。我们发现,模块2捕获的28个行为中的9个行为以及模块3捕获的28个行为中的12个足以分别以98.27%和97.66%的准确性检测ASD风险。行为数量减少超过55%,而两个模块之间的准确性损失可忽略不计,这表明计算和统计方法可简化ASD风险检测和筛选。这些结果可能有助于开发用于初步风险评估和/或临床分诊的移动和家长指导的方法,这些方法可以覆盖更大比例的人群,并有助于降低平均检测和诊断年龄。

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