卒中后癫痫预测模型研究进展
Advance in prediction models for post-stroke epilepsy
于跃 1阳勇 2张嘉骏 1王雁2
作者信息
- 1. 青岛市市立医院康复医学科,青岛 266011
- 2. 青岛大学附属医院神经内科,青岛 266000
- 折叠
摘要
卒中后癫痫(PSE)是卒中后的严重并发症,将导致不良的神经功能预后和较高的死亡风险.实现对PSE的准确预测对随机对照试验的开展和临床诊治方案的制订都具有重要意义.文中对PSE的定义、流行病学现状、危险因素予以概述,对PSE风险预测模型的研究进展进行详细阐述,以及对基于多模态数据集结合机器学习算法的建模方式进行展望,以期实现对PSE的精准预测.
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.
关键词
癫痫/卒中/危险因素/风险评估/机器学习Key words
Epilepsy/Stroke/Risk factors/Risk assessment/Machine learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据