Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.

Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of ep...

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Autores principales:	Rubén Armañanzas, Lidia Alonso-Nanclares, Jesús Defelipe-Oroquieta, Asta Kastanauskaite, Rafael G de Sola, Javier Defelipe, Concha Bielza, Pedro Larrañaga
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/07b67b0f32464265bc7759cb2a5ca409
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spelling	oai:doaj.org-article:07b67b0f32464265bc7759cb2a5ca4092021-11-18T07:47:22ZMachine learning approach for the outcome prediction of temporal lobe epilepsy surgery.1932-620310.1371/journal.pone.0062819https://doaj.org/article/07b67b0f32464265bc7759cb2a5ca4092013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23646148/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.Rubén ArmañanzasLidia Alonso-NanclaresJesús Defelipe-OroquietaAsta KastanauskaiteRafael G de SolaJavier DefelipeConcha BielzaPedro LarrañagaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e62819 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rubén Armañanzas Lidia Alonso-Nanclares Jesús Defelipe-Oroquieta Asta Kastanauskaite Rafael G de Sola Javier Defelipe Concha Bielza Pedro Larrañaga Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.
description	Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.
format	article
author	Rubén Armañanzas Lidia Alonso-Nanclares Jesús Defelipe-Oroquieta Asta Kastanauskaite Rafael G de Sola Javier Defelipe Concha Bielza Pedro Larrañaga
author_facet	Rubén Armañanzas Lidia Alonso-Nanclares Jesús Defelipe-Oroquieta Asta Kastanauskaite Rafael G de Sola Javier Defelipe Concha Bielza Pedro Larrañaga
author_sort	Rubén Armañanzas
title	Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.
title_short	Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.
title_full	Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.
title_fullStr	Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.
title_full_unstemmed	Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.
title_sort	machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/07b67b0f32464265bc7759cb2a5ca409
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Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.

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