Vegetative state: Early prediction of clinical outcome by artificial neural network

L. Pignolo; F. Riganello; A. Candelieri; V. Lagani

Vegetative state: Early prediction of clinical outcome by artificial neural network

L. Pignolo, F. Riganello, A. Candelieri, V. Lagani

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Artificial Neural Networks and Intelligent Information Processing - Proc. 5th Int. Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP 2009, held with ICINCO 2009
Pages	91-96
Number of pages	6
State	Published - Dec 1 2009
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2023-09-23

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title = "Vegetative state: Early prediction of clinical outcome by artificial neural network",

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.",

author = "L. Pignolo and F. Riganello and A. Candelieri and V. Lagani",

note = "Generated from Scopus record by KAUST IRTS on 2023-09-23",

year = "2009",

month = dec,

day = "1",

language = "English (US)",

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Vegetative state: Early prediction of clinical outcome by artificial neural network. / Pignolo, L.; Riganello, F.; Candelieri, A. et al.
Artificial Neural Networks and Intelligent Information Processing - Proc. 5th Int. Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP 2009, held with ICINCO 2009. 2009. p. 91-96.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Vegetative state: Early prediction of clinical outcome by artificial neural network

AU - Pignolo, L.

AU - Riganello, F.

AU - Candelieri, A.

AU - Lagani, V.

N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23

PY - 2009/12/1

Y1 - 2009/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=74549143689&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9789896740023

SP - 91

EP - 96

BT - Artificial Neural Networks and Intelligent Information Processing - Proc. 5th Int. Workshop on Artificial Neural Networks and Intelligent Information Processing - ANNIIP 2009, held with ICINCO 2009

ER -

Vegetative state: Early prediction of clinical outcome by artificial neural network

Abstract

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