Evaluating atypical language in autism using automated language measures.

Evaluating atypical language in autism using automated language measures.
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DOI:
10.1038/s41598-021-90304-5
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发表时间:
2021-05-26
期刊:
影响因子:
4.6
通讯作者:
Fombonne E
Fombonne E
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Salem AC;MacFarlane H;Adams JR;Lawley GO;Dolata JK;Bedrick S;Fombonne E

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测量自闭症谱系障碍(ASD)的语言非典型性是繁琐和昂贵的。需要更好的语言结果衡量标准。使用语言成绩单,我们生成了自动语言测量(ALMS)并测试了它们的有效性。169名7-17岁的受试者(96名自闭症患者,28名多动症患者,45名ADHD患者)接受了孤独症诊断观察表的评估。分析一项任务的成绩单,以产生七种施舍:语素的平均话语长度、不同词根的数量(NDWR)、um比例、内容迷宫比例、不可理解比例、每分钟C-单位和重复比例。除重复比例(P)外,非参数方差分析显示出显著的组间差异(P)。TD组和ADHD组在事后分析中没有不同。除NDWR外,ASD组得分均显著低于两个对照组(P<0.05)。施舍与ASD的标准化临床和语言评估相关。在年龄和智商调整的Logistic回归分析中,4个ALM显著预测ASD状态,准确率令人满意(67.9-75.5%)。当施舍结合在一起时,准确率提高到82.4%。这些施舍为产生新的结果衡量标准提供了一种很有前途的方法。
Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p ), nonparametric ANOVAs showed significant group differences (p). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9–75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.
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