区分儿科双极性障碍,注意力缺陷/多动性障碍和其他精神病学,使用自我报告的情绪和能量数据和动态图发现:基于情绪严重性的相关性和机器学习预测
Rasim S Diler1, Farzan Vahedifard1, Boris Birmaher1
1Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, United States, 1 3125136325.
JMIR mental health
|December 4, 2025
概括
结合动态图和能量数据可以帮助识别患有双相情绪障碍 (BD) 和注意力缺陷/多动症障碍 (ADHD) 的青少年的严重情绪日,有助于早期风险检测.
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.
- 数字健康数字健康
背景情况:
- 由于症状重叠,区分儿科双极性障碍 (BD) 和注意力缺陷/多动性障碍 (ADHD) 具有挑战性.
- 与主观情绪和能量数据相结合,像动态图这样的客观措施可以提高诊断准确性和监测.
研究的目的:
- 调查青少年在动图学衍生活动极端和自我报告的情绪/能量之间的关联.
- 确定这些综合措施是否可以预测患有BD,ADHD或其他诊断的青少年在同一天或第二天发生严重情绪发作.
主要方法:
- 分析了来自四个诊断组的209名住院患者的数据 (ADHD没有BD,BD有ADHD,BD没有ADHD,其他诊断).
- 使用动态图 (Philips Actiwatch 2) 来量化日常活动极端 (最大和最小四分位数).
- 采用机器学习模型,使用动图,睡眠,能量和人口统计数据对严重情绪日进行分类.
主要成果:
- 患有BD但没有ADHD的青少年显示出极端活动与负面情绪/能量之间最强的联系.
- 机器学习模型在预测当天的严重情绪 (ROC-AUC=0.85) 中获得了高准确度 (0.79).
- 能量变化和动图指标 (平均值/峰值) 是情绪事件的关键预测指标.
结论:
- 整合行动图,睡眠和能量评级,可以有效地识别住院青少年的严重情绪日.
- 这种方法为基于可穿戴设备的表型和精确监测提供了潜力.
- 建议在门诊环境中进行外部验证,以获得更广泛的适用性.
关键词:
更多关于 ADHD ADHD 的文章动画摄影 (actigraphy) 是一种摄影方式.青少年 青少年 青少年注意力缺陷/多动症障碍.双相情感障碍是双相情感障碍的一种疾病.能源的能量是能量的能量.机器学习是机器学习.情绪 情绪 情绪 情绪更多相关视频
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