EDEN：一个用于行政索赔数据中乳腺癌复发注释的事件检测网络
EDEN : An Event DEtection Network for the annotation of Breast Cancer recurrences in administrative claims data
摘要：
虽然大量行政索赔数据的出现为研究提供了机会，但由于缺乏与疾病结局相关的临床注释，例如乳腺癌（BC）复发，这些数据的使用仍然受到限制。在行政索赔中对这些终点的注释带来了一些挑战，包括需要推断复发的发生和日期，数据的正确删失，或医疗访问之间的时间间隔的重要性。深度学习方法已成功用于标记时间医学序列，但目前没有方法能够同时处理右删失和访问时间性以检测医学序列中的生存事件。我们提出了EDEN（事件检测网络），一个时间感知的长短期记忆网络的生存分析，和它的自定义损失函数。我们的方法在现实世界的BC数据集上优于几种最先进的方法。EDEN构成了一个强大的工具，注释疾病复发的行政索赔，从而铺平了道路，大规模使用这些数据在不列颠哥伦比亚省的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
