Prospective validation of a seizure diary forecasting falls short.
Epilepsia
; 65(6): 1730-1736, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38606580
ABSTRACT
OBJECTIVE:
Recently, a deep learning artificial intelligence (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 (BSSs) compared random forecasts and simple moving average forecasts to the AI.RESULTS:
The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts.SIGNIFICANCE:
The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Convulsiones
/
Predicción
Límite:
Adult
/
Aged
/
Female
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Humans
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Male
/
Middle aged
Idioma:
En
Revista:
Epilepsia
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos