Use of machine learning to shorten observation-based screening and diagnosis of autism.
Use of machine learning to shorten observation-based screening and diagnosis of autism.
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DOI：
10.1038/tp.2012.10
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发表时间：
2012-04-10
影响因子：
6.8
通讯作者：
Fusaro VA
中科院分区：
文献类型：
--
作者：
Wall DP;Kosmicki J;Deluca TF;Harstad E;Fusaro VA
自闭症诊断观察表（ADOS）是自闭症谱系障碍行为评估中使用最广泛的工具之一。它由四个模块组成，每个模块都是根据语言和发展水平为特定群体量身定制的。平均而言，一个模块需要30到60分钟才能交付。我们使用了一系列机器学习算法来研究自闭症遗传资源交换中心（AGRE）提供的ADOS模块1的完整评分集，其中包括612名患有自闭症的个体和15名来自AGRE和波士顿自闭症联盟（AC）的非谱系个体。我们的分析表明，ADOS模块1中包含的29个项目中的8个足以以100%的准确率对自闭症进行分类。我们进一步验证了这个八项分类器对两个独立来源的完整评分集的准确性，AC的110名自闭症患者和西蒙斯基金会的336名自闭症患者。在这两种情况下，我们的分类器的灵敏度接近100%，除了两个诊断为自闭症的个体外，所有来自这两个资源的个体都被正确分类，并且在观察到的和模拟的非频谱对照的集合上具有94%的特异性。该分类器包含ADOS算法中发现的几个元素，证明了高测试有效性，并且还导致了测量分类置信度和表型极端性的定量得分。随着发病率的上升，有效和快速地对自闭症进行分类的能力需要仔细设计评估和诊断工具。鉴于分类器的简洁性，准确性和定量性，本研究的结果可能被证明是有价值的初步评估和临床优先级的移动的工具的发展，特别是那些专注于评估儿童的家庭短片，加快初步评估的步伐，并扩大到一个显着更大的比例的人口风险。
The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.
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