Multi-channel ConvNet Approach to Predict the Risk of in-Hospital Mortality for ICU Patients

Fabien Viton, Mahmoud Elbattah, Jean-Luc Guérin, Gilles Dequen

2020

Abstract

The healthcare arena has been undergoing impressive transformations thanks to advances in the capacity to capture, store, process, and learn from data. This paper re-visits the problem of predicting the risk of in-hospital mortality based on Time Series (TS) records emanating from ICU monitoring devices. The problem basically represents an application of multi-variate TS classification. Our approach is based on utilizing multiple channels of Convolutional Neural Networks (ConvNets) in parallel. The key idea is to disaggregate multi-variate TS into separate channels, where a ConvNet is used to extract features from each univariate TS individually. Subsequently, the features extracted are concatenated altogether into a single vector that can be fed into a standard MLP classification module. The approach was experimented using a dataset extracted from the MIMIC-III database, which included about 13K ICU-related records. Our experimental results show a promising accuracy of classification that is competitive to the state-of-the-art.

Paper Citation

in Harvard Style

Viton F., Elbattah M., Guérin J. and Dequen G. (2020). Multi-channel ConvNet Approach to Predict the Risk of in-Hospital Mortality for ICU Patients.In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-441-1, pages 98-102. DOI: 10.5220/0009891900980102

in Bibtex Style

@conference{delta20,
author={Fabien Viton and Mahmoud Elbattah and Jean-Luc Guérin and Gilles Dequen},
title={Multi-channel ConvNet Approach to Predict the Risk of in-Hospital Mortality for ICU Patients},
booktitle={Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2020},
pages={98-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009891900980102},
isbn={978-989-758-441-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Multi-channel ConvNet Approach to Predict the Risk of in-Hospital Mortality for ICU Patients
SN - 978-989-758-441-1
AU - Viton F.
AU - Elbattah M.
AU - Guérin J.
AU - Dequen G.
PY - 2020
SP - 98
EP - 102
DO - 10.5220/0009891900980102