Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution.
Front Neurol 2021;
12:675728. [PMID:
34054713 PMCID:
PMC8155381 DOI:
10.3389/fneur.2021.675728]
[Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
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.
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