Prospective validation of a seizure diary forecasting falls short.

ABSTRACT:

Objective

Recently, a deep learning AI model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm.

Methods

We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSS) compared random forecasts and simple moving average forecasts to the AI.

Results

The AI had an AUC of 0.82. At the group level, the AI outperformed random forecasting (BSS=0.53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed th

SUBMITTER: Goldenholz DM 

PROVIDER: S-EPMC10802655 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature