  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.
