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

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If you have a question about this talk, please contact Mateja Jamnik .

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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 (or 1×1) 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) — was developed using a tailored, domain-specific approach. We specifically design the model 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 22-59% (metric dependent) over the commonly used Long-Short Term Memory (LSTM) network.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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