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Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit

18 Jul 2020arXiv:2007.09483archive 2025-07-28

Emma Rocheteau, Pietro Liò, Stephanie Hyland

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 and MIMIC-IV critical care datasets. The model - which we refer to as Temporal Pointwise Convolution (TPC) - is specifically designed to mitigate common challenges with Electronic Health Records, such as skewness, irregular sampling and missing data. In doing so, we have achieved significant performance benefits of 18-68% (metric and dataset dependent) over the commonly used Long-Short Term Memory (LSTM) network, and the multi-head self-attention network known as the Transformer. By adding mortality prediction as a side-task, we can improve performance further still, resulting in a mean absolute deviation of 1.55 days (eICU) and 2.28 days (MIMIC-IV) on predicting remaining length of stay.

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Code

EmmaRocheteau/eICU-LoS-prediction officialmentioned in paperpytorchMIT report

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Tasks

Length-of-Stay prediction Management Mortality Prediction Predicting Patient Outcomes Remaining Length of Stay

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Predicting Patient Outcomes	eICU Collaborative Research Database	Temporal Pointwise Convolution	Kappa	0.58	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Absolute Position Encodings Adam Attention BPE Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Pointwise Convolution Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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