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.

本论文提出了一种基于时态卷积和点卷积（1x1卷积）相结合的新型深度学习模型，旨在解决医院床位管理中的ICU床位管理问题，并成功在eICU重症监护数据集上进行了病人住院天数的预测。与常用的LSTM网络和Transformer相比，本模型可以显著提高18-51%的性能。

使用时序点卷积网络预测重症监护室停留时间