Advance in prediction models for post-stroke epilepsy

VernacularTitle:卒中后癫痫预测模型研究进展
Author: Yue YU ¹ ; Yong YANG ; Jiajun ZHANG ; Yan WANG
Author Information

1. 青岛市市立医院康复医学科，青岛　266011
Keywords: Epilepsy; Stroke; Risk factors; Risk assessment; Machine learning
From: Chinese Journal of Neurology 2024;57(8):915-921
CountryChina
Language:Chinese
Abstract: Post-stroke epilepsy (PSE) is a severe complication that occurs after a stroke and is associated with adverse neurological outcomes and a higher risk of mortality. Achieving an accurate prediction of PSE holds significant importance for the execution of randomized controlled trials and the development of clinical diagnosis and treatment protocols. This article provides an overview of PSE, including its definition, current epidemiological status, and associated risk factors. It also offers a detailed account of the progress in PSE risk prediction models. Furthermore, it looks ahead to the construction of predictive models using a multimodal dataset combined with machine learning algorithms, with the goal of achieving precise prediction of PSE.