The progress in predictive modeling of post-stroke epilepsy By Hao Chen Lei Ge July 9, 2026 0 min Frontiers In Neurology Share Summary Takeaways Listen Insight Report Clinical Scorecard Poll Top Institutions 1 Post-stroke epilepsy (PSE) affects 2–14% of ischemic stroke survivors and 10–20% following hemorrhagic strokes. 2 Accurate prediction of PSE risk is crucial for early intervention and tailored management in stroke patients. 3 Multiple predictive models exist for PSE, focusing on lesion characteristics and early seizure occurrence in different stroke types. 4 Recent machine learning approaches have improved predictive accuracy for PSE, although further validation is needed for clinical use. 5 Integration of multimodal data may enhance seizure prediction and guide personalized intervention strategies for PSE.
