从围产期特征早期预测ADHD症状:一项机器学习研究
Yee-Lam Ho1, Bonnie Auyeung1, Aja Murray1
1Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburghhttps://ror.org/01nrxwf90, Edinburgh, UK.
Development and psychopathology
|November 10, 2025
概括
通过使用出生数据,可以早期识别注意力缺陷多动症 (ADHD) 症状. 在这项研究中,婴儿的男性性别和孕妇在怀孕期间吸烟是该研究的关键预测因素.
科学领域:
- 儿科 儿科 儿科
- 神经发育障碍 神经发育障碍
- 公共卫生 公共卫生
背景情况:
- 早期识别注意力缺陷多动症 (ADHD) 症状对于及时干预和改善长期结果至关重要.
- 以前的研究已经确定了与ADHD相关的各种孕产妇和产周因素,但对这些预测因子进行集体检查的全面分析是有限的.
- 了解早期的风险因素可以为预防策略和对受影响儿童和家庭的支持提供信息.
研究的目的:
- 根据出生时可获得的信息,评估晚期ADHD症状的可预测性,包括种族,母亲的代谢标志物,心理健康和社会经济地位.
- 通过对母婴和围产期数据的综合分析,确定最有影响力的ADHD症状预测因素.
- 通过使用例行收集的数据,建立早期ADHD风险识别的原则证明.
主要方法:
- 利用了来自布拉德福德出生 (BiB) 队列研究的数据.
- 应用多重线性回归 (LR) 和机器学习技术 (随机森林) 来预测ADHD症状.
- 使用强项和困难问卷 (SDQ) 的过度活动/注意力缺失子表来测量ADHD症状.
主要成果:
- 一个经过十倍交叉验证的LR模型解释了SDQ分数中6.97%的差异.
- 随机森林模型确定了婴儿男性性别和怀孕期间母亲吸烟是ADHD症状最重要的预测因素.
- 这些发现表明,使用与出生有关的信息用于早期风险评估的潜力.
结论:
- 这项研究为早期识别有患ADHD症状风险的儿童提供了原则证明.
- 婴儿性行为和怀孕期间母亲吸烟是ADHD症状的关键早期预测因素.
- 将额外的围产期数据纳入预测模型可能会提高早期ADHD风险评估的准确性.
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