Abstract：
UNASSIGNED: Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12-24 months, mother\'s age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life.
摘要：
■及时识别自闭症谱系疾病是使儿童从早期干预中获得最大益处的必要条件。新兴的技术进步为检测微妙的，自闭症的早期指标来自常规收集的健康信息。这项研究测试了一个模型，该模型从生命的前2年收集的婴儿健康访问记录中提供了自闭症诊断的可能性得分。它包括591,989名非自闭症儿童和12,846名自闭症儿童的记录。该模型确定了自闭症谱系疾病组的三分之二（男孩63％和女孩66％）。性别特异性模型有几个共同的预测特征。这些包括语言发展，精细的运动技能，以及12-24个月访问的社会里程碑，母亲的年龄，和较低的初始增长，但较高的最后增长测量。父母对发育或听力障碍的担忧是其他预测因素。这些模型在其他生长测量和出生参数方面有所不同。这些模型可以通过使用在生命的头两年中常规记录的信息来支持女孩和男孩自闭症的早期迹象的检测。
