在患有严重精神疾病的个体中,个性化预测负面影响,随后使用长期多式联络移动表型化
Christian A Webb1,2, Boyu Ren3,4, Habiballah Rahimi-Eichi3,4
1Harvard Medical School, Department of Psychiatry, Boston, MA, USA. cwebb@mclean.harvard.edu.
Translational psychiatry
|May 19, 2025
概括
智能手机可以利用传感器数据预测严重精神疾病患者的负面情绪. 机器学习模型,特别是个性化的机器学习模型,对实时检测和干预充满希望.
科学领域:
- 数字表型化和心理健康
- 计算精神病学是一种计算精神病学.
- 医疗保健中的机器学习
背景情况:
- 强烈的负面情绪是严重精神疾病的主要特征.
- 智能手机通过传感器提供连续的,不引人注目的行为数据收集.
- 行为数据可以预测负面影响的实时增加.
研究的目的:
- 评估智能手机传感器数据在预测增加负面影响方面的有效性.
- 为了比较各种统计方法,包括个性化机器学习,进行预测.
- 探索实时检测负面情绪状态的潜力.
主要方法:
- 68名患有情绪或精神障碍的成年人提供了超过一年的日常情绪调查.
- 连续收集智能手机数据 (加速计,GPS,屏幕使用情况) 和可穿戴设备数据.
- 开发了一个个性化的整体机器学习算法,并与其他方法进行了比较.
主要成果:
- 个性化的集体机器学习算法实现了AUC值为0.72 (易怒) 到0.79 (孤独).
- 智能手机GPS位置数据成为最具预测性的功能集.
- 在智能手机数据与负面影响之间的关系中观察到显著的个体异质性.
结论:
- 智能手机和机器学习可以有效地检测严重精神疾病患者的高度负面情绪.
- 个性化建模至关重要,因为情绪状态的数字标记在个人之间的差异.
- 实时预测可以实现基于智能手机的心理健康干预的及时,自动交付.
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