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Prediction of Seizure Recurrence. A Note of Caution
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We a...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155381/ https://www.ncbi.nlm.nih.gov/pubmed/34054713 http://dx.doi.org/10.3389/fneur.2021.675728 |
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author | Bosl, William J. Leviton, Alan Loddenkemper, Tobias |
author_facet | Bosl, William J. Leviton, Alan Loddenkemper, Tobias |
author_sort | Bosl, William J. |
collection | PubMed |
description | Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism. |
format | Online Article Text |
id | pubmed-8155381 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81553812021-05-28 Prediction of Seizure Recurrence. A Note of Caution Bosl, William J. Leviton, Alan Loddenkemper, Tobias Front Neurol Neurology Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism. Frontiers Media S.A. 2021-05-13 /pmc/articles/PMC8155381/ /pubmed/34054713 http://dx.doi.org/10.3389/fneur.2021.675728 Text en Copyright © 2021 Bosl, Leviton and Loddenkemper. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Bosl, William J. Leviton, Alan Loddenkemper, Tobias Prediction of Seizure Recurrence. A Note of Caution |
title | Prediction of Seizure Recurrence. A Note of Caution |
title_full | Prediction of Seizure Recurrence. A Note of Caution |
title_fullStr | Prediction of Seizure Recurrence. A Note of Caution |
title_full_unstemmed | Prediction of Seizure Recurrence. A Note of Caution |
title_short | Prediction of Seizure Recurrence. A Note of Caution |
title_sort | prediction of seizure recurrence. a note of caution |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155381/ https://www.ncbi.nlm.nih.gov/pubmed/34054713 http://dx.doi.org/10.3389/fneur.2021.675728 |
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