论文标题
通过暂时的卷积网络预测重症监护室的住宿时间
Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks
论文作者
论文摘要
越来越多的患者需求和预算限制的压力使医院的床位管理成为临床人员的每日挑战。最关键的是将资源过度重症监护室（ICU）床的有效分配给需要生活支持的患者。解决此问题的核心是知道当前的ICU患者可能会停留多长时间。在这项工作中，我们提出了一个基于时间卷积和点式卷积（1x1）卷积的组合的新的深度学习模型，以解决EICU重症监护数据集中的停留预测任务。该模型（我们称为时间旋转卷积（TPC））是专门设计的，目的是通过电子健康记录（例如偏斜，不规则采样和缺失数据）来缓解常见的挑战。在此过程中，我们已经获得了18-51％（指标依赖性）的显着性能优势，而不是常用的长期记忆（LSTM）网络，而多头自发项网络（称为变压器）。
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to the patients who need life support. Central to solving this problem is knowing for how long the current set of ICU patients are likely to stay in the unit. In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU critical care dataset. The model - which we refer to as Temporal Pointwise Convolution (TPC) - is specifically designed to mitigate for common challenges with Electronic Health Records, such as skewness, irregular sampling and missing data. In doing so, we have achieved significant performance benefits of 18-51% (metric dependent) over the commonly used Long-Short Term Memory (LSTM) network, and the multi-head self-attention network known as the Transformer.
