Multi-channel ConvNet approach to predict the risk of in-hospital mortality for ICU patients

Viton, Fabien; Elbattah, Mahmoud; Gu�rin, Jean-Luc; Dequen, Gilles

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.

Citation

Viton, F., Elbattah, M., Guérin, J., & 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 (98-102). https://doi.org/10.5220/0009891900980102

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 1st International Conference on Deep Learning Theory and Applications
• Start Date: Jul 8, 2020
• End Date: Jul 10, 2020
• Acceptance Date: May 22, 2020
• Publication Date: 2020
• Deposit Date: May 11, 2022
• Pages: 98-102
• Book Title: Proceedings of the 1st International Conference on Deep Learning Theory and Applications
• ISBN: 9789897584411
• DOI: https://doi.org/10.5220/0009891900980102
• Public URL: https://uwe-repository.worktribe.com/output/9206333