  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.
