Fabien Viton
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
Authors
Mahmoud Elbattah
Jean-Luc Gu�rin
Gilles Dequen
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 |
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