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

arXiv: https://arxiv.org/abs/2007.09483 · HTML: https://mlsanity.com/paper/2007.09483/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [Predicting Patient Outcomes with Graph Representation Learning](https://mlsanity.com/paper/2101.03940/) (2021)
- [DySurv: dynamic deep learning model for survival analysis with conditional variational inference](https://mlsanity.com/paper/2310.18681/) (2023)
- [Looking for Out-of-Distribution Environments in Multi-center Critical Care Data](https://mlsanity.com/paper/2205.13398/) (2022)
- [Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit](https://mlsanity.com/paper/2306.01970/) (2023)
- [XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients](https://mlsanity.com/paper/2305.06109/) (2023)
- [Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Health](https://mlsanity.com/paper/2502.16477/) (2025)
- [The CRITICAL Records Integrated Standardization Pipeline (CRISP): End-to-End Processing of Large-scale Multi-institutional OMOP CDM Data](https://mlsanity.com/paper/2509.08247/) (2025)
- [Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling](https://mlsanity.com/paper/2604.18753/) (2026)
- [Client Recruitment for Federated Learning in ICU Length of Stay Prediction](https://mlsanity.com/paper/2304.14663/) (2023)
- [Length of Stay prediction for Hospital Management using Domain Adaptation](https://mlsanity.com/paper/2306.16823/) (2023)
- [XAI for In-hospital Mortality Prediction via Multimodal ICU Data](https://mlsanity.com/paper/2312.17624/) (2023)

## Abstract (arXiv)

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.