Integrating deep neural networks with the Hawkes process has significantly improved predictive capabilities in finance, health informatics, and information technology. Nevertheless, these models often face challenges in real-world settings, particularly due to substantial label noise. This issue is of significant concern in the medical field, where label noise can arise from delayed updates in electronic medical records or misdiagnoses, leading to increased prediction risks. Our research indicates that deep Hawkes process models exhibit reduced robustness when dealing with label noise, particularly when it affects both event types and timing. To address these challenges, we first investigate the influence of label noise in approximated intensity functions and present a novel framework, the Robust Deep Hawkes Process (RDHP), to overcome the impact of label noise on the intensity function of Hawkes models, considering both the events and their occurrences. We tested RDHP using multiple open-source benchmarks with synthetic noise and conducted a case study on obstructive sleep apnea-hypopnea syndrome (OSAHS) in a real-world setting with inherent label noise. The results demonstrate that RDHP can effectively perform classification and regression tasks, even in the presence of noise related to events and their timing. To the best of our knowledge, this is the first study to successfully address both event and time label noise in deep Hawkes process models, offering a promising solution for medical applications, specifically in diagnosing OSAHS.

翻译：将深度神经网络与霍克斯过程相结合，显著提升了金融、健康信息学及信息技术领域的预测能力。然而，这些模型在现实场景中常面临挑战，尤其是由于显著的标签噪声问题。这一问题在医疗领域尤为重要，标签噪声可能源于电子病历的延迟更新或误诊，从而导致预测风险增加。我们的研究表明，深度霍克斯过程模型在处理标签噪声时稳健性降低，特别是当噪声同时影响事件类型和发生时间时。为应对这些挑战，我们首先研究了标签噪声对近似强度函数的影响，并提出了一种新颖的框架——稳健深度霍克斯过程（RDHP），以克服标签噪声对霍克斯模型强度函数的影响，同时考虑事件及其发生时间。我们使用多个带有合成噪声的开源基准测试了RDHP，并在现实场景中对阻塞性睡眠呼吸暂停低通气综合征（OSAHS）进行了案例研究，该场景存在固有的标签噪声。结果表明，即使在存在事件及其时间相关噪声的情况下，RDHP仍能有效执行分类和回归任务。据我们所知，这是首个成功解决深度霍克斯过程模型中事件与时间标签噪声的研究，为医疗应用（特别是OSAHS诊断）提供了有前景的解决方案。