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Authors: L. Pignolo 1 ; F. Riganello 1 ; A. Candelieri 2 and V. Lagani 2

Affiliations: 1 S. Anna Institute, Italy ; 2 University of Calabria, Italy

Keyword(s): Artificial Intelligence, Vegetative State, Clinical Outcome, Prognosis.

Abstract: Residual brain function has been documented in vegetative state patients, yet early prognosis remains difficult. Purpose of this study was to identify by artificial Neural Network procedures the significant neurological signs correlated to, and predictive of outcome. The best networks test set accuracy was 70%, 72% and 70% for the entire patients’ group and the posttraumatic and non-posttraumatic subgroups, respectively. The method accuracy does not reflect a perfect classification, but is significantly far from the random or educated guess and is in accordance with the results of previous clinical studies.

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Paper citation in several formats:
Pignolo, L.; Riganello, F.; Candelieri, A. and Lagani, V. (2009). Vegetative State: Early Prediction of Clinical Outcome by Artificial Neural Network. In Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2009) - Workshop ANNIIP; ISBN 978-989-674-002-3, SciTePress, pages 91-96. DOI: 10.5220/0002264300910096

@conference{workshop anniip09,
author={L. Pignolo. and F. Riganello. and A. Candelieri. and V. Lagani.},
title={Vegetative State: Early Prediction of Clinical Outcome by Artificial Neural Network},
booktitle={Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2009) - Workshop ANNIIP},
year={2009},
pages={91-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002264300910096},
isbn={978-989-674-002-3},
}

TY - CONF

JO - Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2009) - Workshop ANNIIP
TI - Vegetative State: Early Prediction of Clinical Outcome by Artificial Neural Network
SN - 978-989-674-002-3
AU - Pignolo, L.
AU - Riganello, F.
AU - Candelieri, A.
AU - Lagani, V.
PY - 2009
SP - 91
EP - 96
DO - 10.5220/0002264300910096
PB - SciTePress

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