EDEN : An Event DEtection Network for the annotation of Breast Cancer recurrences in administrative claims data
This addresses the need for accurate recurrence detection in breast cancer research using administrative data, enabling broader use of such data, though it is an incremental improvement in method application.
The paper tackled the problem of annotating breast cancer recurrences in administrative claims data, which lacks clinical annotations, by proposing EDEN, a time-aware LSTM network for survival analysis that outperforms state-of-the-art approaches on real-world datasets.
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.