早期识别患有注意力缺陷/多动障碍 (ADHD) 的儿童
Yang S Liu1,2, Fernanda Talarico1,2, Dan Metes2
1Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.
PLOS digital health
|November 7, 2024
概括
机器学习模型现在可以使用健康数据和发育工具预测幼儿的注意力缺陷/多动障碍 (ADHD). 这种方法有助于早期识别ADHD,改善干预机会.
科学领域:
- 儿科健康 儿科健康
- 医疗保健中的机器学习
- 发展心理学 发展心理学
背景情况:
- 注意缺陷/多动症 (ADHD) 症状通常在学前班出现,但经常被忽视,延迟了关键的早期干预.
- 早期发现ADHD对于有效管理和改善儿童的长期结果至关重要.
- 人口级数据提供了一种可扩展的方法来识别像ADHD这样的发育风险.
研究的目的:
- 开发和验证用于幼儿园儿童早期预测ADHD的机器学习模型.
- 评估将行政健康数据与早期发育仪器 (EDI) 结合用于ADHD检测的实用性.
- 通过使用人口级数据,确定ADHD的关键预测因素.
主要方法:
- 分析了23494名没有先前被诊断为ADHD的幼儿园儿童的队列.
- 机器学习模型被训练并使用行政健康数据和EDI分数进行测试.
- 用四年的随访期来确定发生的ADHD病例 (1680名儿童).
主要成果:
- 最好的机器学习模型在预测ADHD时实现了0.811的曲线下面面积 (AUC).
- 关键预测因素包括EDI子域分数,孩子的性别和社会经济地位.
- 该模型使用集成数据源证明了可靠的ADHD前性预测.
结论:
- 整合行政和EDI监控数据的机器学习算法显示出早期ADHD识别的前景.
- 这种方法可以支持对有ADHD风险的儿童进行及时干预.
- 人口级数据分析为积极的儿童健康监测提供了强有力的策略.
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