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Communication dans un congrès

Multi-Channel Convnet Approach to Predict the Risk of in-Hospital Mortality for Icu Patients

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. \textcopyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
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Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : vendredi 24 juin 2022 - 15:28:47
Dernière modification le : vendredi 5 août 2022 - 11:22:19


  • HAL Id : hal-03704093, version 1



F. Viton, M. Elbattah, Jean-Luc Guérin, Gilles Dequen. Multi-Channel Convnet Approach to Predict the Risk of in-Hospital Mortality for Icu Patients. DeLTA 2020 - Proceedings of the 1st International Conference on Deep Learning Theory and Applications, Jul 2020, A distance, Unknown Region. pp.98--102. ⟨hal-03704093⟩



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