  While the emergence of large administrative claims data provides
opportunities for research, their use remains limited by the lack of clinical
annotations relevant to disease outcomes, such as recurrence in breast cancer
(BC). Several challenges arise from the annotation of such endpoints in
administrative claims, including the need to infer both the occurrence and the
date of the recurrence, the right-censoring of data, or the importance of time
intervals between medical visits. Deep learning approaches have been
successfully used to label temporal medical sequences, but no method is
currently able to handle simultaneously right-censoring and visit temporality
to detect survival events in medical sequences. We propose EDEN (Event
DEtection Network), a time-aware Long-Short-Term-Memory network for survival
analyses, and its custom loss function. Our method outperforms several
state-of-the-art approaches on real-world BC datasets. EDEN constitutes a
powerful tool to annotate disease recurrence from administrative claims, thus
paving the way for the massive use of such data in BC research.
