LAUSR

OBJECTIVE There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries. METHODS Data from 5,419 patients of SeizureTracker.com (including seizure count, type and duration) was split into training (3,806 patients/1,665,215 patient-days) and testing (1,613 patients/549,588 patient-days) sets with no overlapping patients. An artificial intelligence (AI) program, consisting recurrent networks followed by a multilayer perceptron ("deep learning" model) was trained to produce risk forecasts. Forecasts were made from a sliding window of 3-month diary history for each day of each patient's diary. After training, the model parameters were held constant and the testing set was scored. A rate-matched random (RMR) forecast was compared to the AI. Comparisons were made using the area under the receiver operating characteristic curve (AUC), a measure of binary discrimination performance, and the Brier Score, a measure of forecast calibration. The Brier Skill Score (BSS) measured the improvement of the AI Brier Score compared to the benchmark RMR Brier score. Confidence intervals on performance statistics were obtained via bootstrapping. RESULTS The AUC was 0.86 (0.85-0.88) for AI and 0.83 (0.81-0.85) for RMR, favoring AI (p<0.001). Overall (all patients combined) BSS was 0.27(0.23-0.31), also favoring AI (p<0.001). INTERPRETATION The AI produced a valid forecast superior to a chance forecaster, and provided meaningful forecasts in the majority of patients. Future studies will be needed to quantify the clinical value of these forecasts for patients. This article is protected by copyright. All rights reserved.