Advance in prediction models for post-stroke epilepsy
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.
NETL
NSTL
万方数据
