计算网络模型用于预测和控制数字应用中的心理健康轨迹
Janik Fechtelpeter1,2,3,4, Christian Rauschenberg5,6, Christian Goetzl7
1Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University (UHD), Heidelberg, Germany. janik.fechtelpeter@zi-mannheim.de.
NPJ digital medicine
|December 30, 2025
概括
非线性模型准确地预测心理状态,为数字心理健康干预提供可解释的见解. 这些先进的模型改善了预测,并指导了个性化的心理健康护理策略.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 数字健康数字健康
背景情况:
- 生态瞬间评估 (EMA) 追踪每天的情绪和行为状态.
- 准确预测这些状态对于适应性心理健康干预至关重要.
- 现有的网络模型经常假设线性,缺乏预测评估.
研究的目的:
- 为了对线性网络模型进行基准测试,非线性状态空间模型 (SSM) 使用零碎线性循环神经网络 (PLRNNs) 和变压器.
- 评估模型在预测EMA数据的心理动态方面的表现.
- 评估干预规划中非线性模型的可解释性和实用性.
主要方法:
- 利用了三项40天微型随机试验 (N=145) 的数据.
- 比较线性网络模型,非线性SSM (PLRNNs) 和变压器.
- 评估了自发和干预驱动动力学的预测准确性.
主要成果:
- 在预测EMA动态方面,PLRNN表现出卓越的准确性.
- 在PLRNN中确定了可以在心理上解释的连接模式,突出了关键的情感节点.
- 使用推断动态的模拟将网络结构与预测和干预计划联系起来.
结论:
- 非线性SSM,特别是PLRNN,提供心理状态的准确和可解释的预测.
- 这些模型为数字心理健康的实时控制和个性化干预提供了实际基础.
- 实时再培训和不完整数据的强大性能支持它们的临床实用性.
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