loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Fabien Viton ; Mahmoud Elbattah ; Jean-Luc Guérin and Gilles Dequen

Affiliation: Laboratoire MIS, Université de Picardie Jules Verne, Amiens, France

Keyword(s): ConvNet, Deep Learning, Multivariate Time Series, in-Hospital Mortality.

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 classificatio n that is competitive to the state-of-the-art. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.221.239.148

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - DeLTA; ISBN 978-989-758-441-1, SciTePress, pages 98-102. DOI: 10.5220/0009891900980102

@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 - DeLTA},
year={2020},
pages={98-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009891900980102},
isbn={978-989-758-441-1},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - 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
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic