双极性障碍住院青少年的最大和最小活动:使用人工智能的动图图案的每日变量分类
Farzan Vahedifard1, Boris Birmaher1,2, Satish Iyengar3
1Department of Psychiatry, University of Pittsburgh School of Medicine, USA.
Psychiatry research communications
|June 27, 2025
概括
使用动图和人工智能进行客观的日常活动测量可以帮助区分青少年双相情感障碍 (BD) 和注意力缺陷/多动症障碍 (ADHD). 这种方法在分类诊断组中实现了91.5%的准确性,为改善诊断精度提供了潜力.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 计算机科学 计算机科学
背景情况:
- 青少年双相情感障碍 (BD) 诊断可能具有挑战性.
- 需要客观标记来区分BD与注意力缺陷/多动症障碍 (ADHD).
- 动图提供了一种潜在的客观衡量日常活动的方法.
研究的目的:
- 调查动图数据和人工智能的实用性,以对青少年精神病诊断进行分类.
- 要区分没有ADHD的BD,有ADHD的BD,没有BD的ADHD和其他诊断 (OD).
主要方法:
- 利用了389名住院青少年 (2014-2023) 的图表审查的动图数据.
- 采用人工智能方法,包括随机森林和XGBoost模型,在时间序列活动数据上进行特征工程.
- 在包含5193天活动数据的数据集上验证模型.
主要成果:
- 具有特征选择的XGBoost模型在对诊断组进行分类时实现了91.5%的准确性.
- 最有影响力的特征是日常活动高峰时间之间的工程差异.
- 与年龄相结合的动图学衍生活动模式,被证明是诊断分类的有效方法.
结论:
- 动图和机器学习提供了一个有希望的,客观的方法来分类青少年精神病医院住院患者的诊断群体.
- 每小时活动的工程特征,特别是可变性,可以作为客观标记来提高诊断准确性.
- 为了评估临床影响,需要在不同的队列和现实环境中进行进一步的验证.
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