Aggressive behavior, including aggression towards others and self-injury, occurs in up to 80% of children and adolescents with autism, making it a leading cause of behavioral health referrals and a major driver of healthcare costs. Predicting when autistic youth will exhibit aggression can be challenging due to their communication difficulties. Many are minimally verbal or have poor emotional insight. Recent advances in Machine Learning and wearable biosensing demonstrate the ability to predict aggression within a limited future window (typically one to three minutes) in autistic individuals. However, existing works don't estimate aggression onset probability or the expected number of aggression onsets over longer periods, nor do they provide interpretable insights into onset dynamics. To address these limitations, we apply Temporal Point Processes (TPPs) - particularly self-exciting Hawkes processes - to model the timing of aggressive behavior onsets in psychiatric inpatient autistic youth. We benchmark several TPP models by evaluating their goodness-of-fit and predictive metrics. Our results demonstrate that self-exciting TPPs more accurately captures the irregular and clustered nature of aggression onsets, especially compared to traditional Poisson models. These incipient findings suggest that TPPs can provide interpretable, probabilistic forecasts of aggression onset along a time continuum, supporting future clinical decision-making and preemptive intervention.

翻译：攻击行为（包括对他人的攻击和自伤行为）在高达80%的自闭症儿童及青少年中出现，这使其成为心理健康转诊的主要原因和医疗成本的重要驱动因素。由于自闭症青少年存在沟通障碍，预测其何时会表现出攻击行为极具挑战性。他们中许多人仅具备极低语言能力或缺乏情绪洞察力。机器学习与可穿戴生物传感技术的最新进展表明，可在有限未来窗口（通常为一至三分钟）内预测自闭症个体的攻击行为。然而，现有研究既未评估攻击行为的发作概率或长期内的预期发作次数，也未提供关于发作动力学的可解释性洞见。针对这些局限，我们应用时间点过程（TPP）——特别是自激励霍克斯过程——对自闭症住院青少年攻击行为发作的时间规律进行建模。我们通过评估多个TPP模型的拟合优度与预测指标进行基准测试。结果表明，自激励TPP能更准确地捕捉攻击行为发作的不规则性和聚集性特征，尤其优于传统泊松模型。这些初步发现表明，TPP可在连续时间维度上提供可解释的概率性攻击行为发作预测，为未来的临床决策和预防性干预提供支持。