模拟使用概率性子过程的反复自杀企图
Darío C Larese1, Pablo M Olmos1, Antonio Artés1
1Department of Signal Theory and Communications, Carlos III University, Madrid, Spain.
Spanish journal of psychiatry and mental health
|February 9, 2026
概括
自杀企图的复发集群是暂时的,大多数重复尝试发生在一年内. 基于霍克斯的建模为预防自杀提供了动态的,个性化的风险预测.
科学领域:
- 计算精神病学是一种计算精神病学.
- 在医疗保健中的数学建模.
- 自杀学是一种自杀学.
背景情况:
- 评估自杀企图复发风险至关重要,但具有挑战性.
- 现有的模型通常缺乏使用常规临床数据的动态预测.
- 预测分析在预防自杀的努力中未得到充分利用.
研究的目的:
- 开发和评估一个纵向数学模型来预测自杀企图的复发.
- 利用常规临床数据进行动态风险估计.
- 为了确定自杀企图复发的时间风险概况.
主要方法:
- 分析了来自MCOSUL队列的1,112名个人,至少随访5年.
- 使用了通过最大概率和神经网络估计的霍克斯过程参数.
- 应用高斯混合模型来识别时间风险概况.
主要成果:
- 自杀企图的复发表现出时间聚类,61.1%在1个月内,几乎所有在12个月内.
- 该模型捕获了自我激发的动态,产生了个性化的风险曲线.
- 确定了五个不同的时间风险集群,从低风险到极端风险.
结论:
- 自杀尝试的重复表明自我激发的时间行为.
- 基于霍克斯的建模提供了动态的,随时间变化的风险估计.
- 这种方法可以增强个性化的自杀预防策略.
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