Use of artificial intelligence to shorten the behavioral diagnosis of autism.
Use of artificial intelligence to shorten the behavioral diagnosis of autism.
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DOI：
10.1371/journal.pone.0043855
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Deluca TF
中科院分区：
文献类型：
--
作者：
Wall DP;Dally R;Luyster R;Jung JY;Deluca TF
自闭症诊断访谈-修订版（ADI-R）是辅助自闭症行为诊断最常用的工具之一。该考试包括93道题，必须由护理人员在一个通常长达2.5小时的集中环节内回答。我们使用机器学习技术来研究自闭症基因研究交流（AGRE）上提供的完整的ADI-R答案集，其中包括891名被诊断为自闭症的个体和75名不符合自闭症诊断标准的个体。我们的分析表明，ADI-R中包含的93个项目中有7个足以对自闭症进行分类，统计准确率为99.9%。我们进一步测试了这个包含7个问题的分类器对两个独立来源的完整答案集的准确性，一个来自西蒙斯基金会的1654名自闭症患者的集合，一个来自波士顿自闭症协会的322名自闭症患者的集合。在这两种情况下，我们的分类器的统计准确率接近100%，正确地分类了这两个资源中之前通过标准ADI-R诊断为自闭症的所有个体，只有一人除外。我们测量特异性的能力受到使用的研究数据中少数非频谱病例的限制，然而，真实和模拟数据均显示特异性范围为99%至93.8%。随着发病率的上升，快速有效地诊断自闭症的能力需要仔细设计行为评估方法。我们的研究是对大型数据库进行回顾性分析的初步尝试，以获得一种准确但显著缩短的方法，这种方法可用于可能患有自闭症谱系障碍的个体的快速检测和临床优先排序。这样的工具可以帮助简化临床诊断过程，从而更快地筛查和早期治疗自闭症患者。
The Autism Diagnostic Interview-Revised (ADI-R) is one of the most commonly used instruments for assisting in the behavioral diagnosis of autism. The exam consists of 93 questions that must be answered by a care provider within a focused session that often spans 2.5 hours. We used machine learning techniques to study the complete sets of answers to the ADI-R available at the Autism Genetic Research Exchange (AGRE) for 891 individuals diagnosed with autism and 75 individuals who did not meet the criteria for an autism diagnosis. Our analysis showed that 7 of the 93 items contained in the ADI-R were sufficient to classify autism with 99.9% statistical accuracy. We further tested the accuracy of this 7-question classifier against complete sets of answers from two independent sources, a collection of 1654 individuals with autism from the Simons Foundation and a collection of 322 individuals with autism from the Boston Autism Consortium. In both cases, our classifier performed with nearly 100% statistical accuracy, properly categorizing all but one of the individuals from these two resources who previously had been diagnosed with autism through the standard ADI-R. Our ability to measure specificity was limited by the small numbers of non-spectrum cases in the research data used, however, both real and simulated data demonstrated a range in specificity from 99% to 93.8%. With incidence rates rising, the capacity to diagnose autism quickly and effectively requires careful design of behavioral assessment methods. Ours is an initial attempt to retrospectively analyze large data repositories to derive an accurate, but significantly abbreviated approach that may be used for rapid detection and clinical prioritization of individuals likely to have an autism spectrum disorder. Such a tool could assist in streamlining the clinical diagnostic process overall, leading to faster screening and earlier treatment of individuals with autism.
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影响因子：
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作者：
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