Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age.
Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age.
复制标题
DOI：
10.1016/j.jaac.2020.10.015
复制
发表时间：
2021-08
影响因子：
13.3
通讯作者：
IBIS Network
中科院分区：
文献类型：
--
作者：
Meera SS;Donovan K;Wolff JJ;Zwaigenbaum L;Elison JT;Kinh T;Shen MD;Estes AM;Hazlett HC;Watson LR;Baranek GT;Swanson MR;St John T;Burrows CA;Schultz RT;Dager SR;Botteron KN;Pandey J;Piven J;IBIS Network
本研究旨在根据父母报告的测量（第一年量表v.2.0; FYI）开发12个月大婴儿的分类器，以：（1）将高于ASD家族风险状态的婴儿分类为高风险婴儿;（2）作为改进人群样本风险估计方法的起点。54名高家族风险（HR）婴儿后来被诊断为ASD（HR-ASD），183名HR婴儿在24个月大时未被诊断为ASD（HR-Neg），72名低风险对照参与了这项研究。所有婴儿在12个月大时提供FYI数据，并在24个月大时进行ASD诊断评估。使用数据驱动的交叉验证分析方法来开发分类器，以确定FYI对HR-ASD和HR-Neg进行分类的筛选准确性（例如灵敏度）。新开发的FYI分类器的估计灵敏度为0.71（95% CI：0.50，0.91），特异性为0.72（95% CI：0.49，0.91）。该分类器证明了在已经处于ASD家族风险升高的婴儿中改善目前12个月大ASD风险筛查的潜力，增加了婴儿期自闭症风险检测的机会。这项研究的结果强调了将家长报告措施与机器学习方法相结合的实用性。
This study aimed to develop a classifier for infants at 12 months of age based on a parent-report measure (the First Year Inventory v.2.0; FYI), to: (1) classify infants at elevated risk, above and beyond that attributable to familial risk status for ASD; and, (2) serve as a starting point to refine an approach for risk estimation in population samples. Fifty-four high familial risk (HR) infants later diagnosed with ASD (HR-ASD), 183 HR infants not diagnosed with ASD at 24 months of age (HR-Neg), and 72 low risk controls participated in the study. All infants contributed FYI data at 12 months of age and had a diagnostic assessment for ASD at age 24 months. A data-driven, cross-validated analytic approach was used to develop a classifier to determine screening accuracy (e.g. sensitivity) of the FYI to classify HR-ASD and HR-Neg. The newly developed FYI classifier had an estimated sensitivity of 0.71 (95% CI: 0.50, 0.91) and specificity of 0.72 (95% CI: 0.49, 0.91). This classifier demonstrates the potential to improve current screening for ASD risk at 12 months of age in infants already at elevated familial risk for ASD, increasing opportunities for detection of autism risk in infancy. Findings from this study highlight the utility of combining parent-report measures with machine learning approaches.
登录
查看更多内容
影响因子：
3.9
作者：
Gotham, Katherine;Pickles, Andrew;Lord, Catherine
通讯作者：
Lord, Catherine
影响因子：
17.1
作者：
Emerson RW;Adams C;Nishino T;Hazlett HC;Wolff JJ;Zwaigenbaum L;Constantino JN;Shen MD;Swanson MR;Elison JT;Kandala S;Estes AM;Botteron KN;Collins L;Dager SR;Evans AC;Gerig G;Gu H;McKinstry RC;Paterson S;Schultz RT;Styner M;IBIS Network;Schlaggar BL;Pruett JR Jr;Piven J
通讯作者：
Piven J
影响因子：
7.6
作者：
Green, Jonathan;Pickles, Andrew;Johnson, Mark
通讯作者：
Johnson, Mark
影响因子：
3.9
作者：
Lord, C;Risi, S;Rutter, M
通讯作者：
Rutter, M
影响因子：
1.8
作者：
Kim, Ji-Hyun
通讯作者：
Kim, Ji-Hyun