自闭症适应性行为轨迹的预测建模:来自临床队列研究的见解
Annie Aitken1,2, Maia C Lazerwitz3, Ally Eash3
1Cortica Healthcare, San Diego, CA, USA. aitkenannie@gmail.com.
Translational psychiatry
|October 10, 2025
概括
这项研究确定了自闭症儿童的两个适应性行为轨迹:改善和稳定. 机器学习模型使用初始临床和人口统计数据准确预测了这些轨迹,突出了关键影响因素.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 机器学习 机器学习
背景情况:
- 了解适应性行为轨迹对于为神经发育差异的儿童量身定制干预措施至关重要.
- 以前的研究还没有充分利用基线数据来预测自闭症的个体发展路径.
研究的目的:
- 描述自闭症儿童的适应性行为轨迹.
- 开发机器学习模型,使用初始临床和人口统计数据预测这些轨迹.
主要方法:
- 隐性类增长混合建模 (LCGMM) 分析了1225名自闭症儿童 (20-90个月) 的Vineland适应性行为尺度得分.
- 机器学习算法 (弹性网GLM,SVM,随机森林) 使用729名儿童的摄入数据预测了轨迹.
主要成果:
- 两个不同的轨迹出现了:"较少的损伤/改善" (≥66%) 和"较高的损伤/稳定" (≤33%).
- 一个随机森林模型在预测轨迹方面取得了77%的准确性.
- 关键预测因素包括社会经济地位,发育回归,气质,父亲年龄,自闭症严重程度和同时出现的ADHD症状.
结论:
- 基线临床和人口因素显著预测自闭症儿童的适应性行为轨迹.
- 机器学习模型可以个性化治疗程序开发.
- 增加治疗时间并没有显著提高预测准确度,这表明需要个性化干预策略超越数量.
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