危险分层
冲程(发动机)
医学
疾病
机器学习
分层(种子)
人口
算法
内科学
计算机科学
工程类
环境卫生
机械工程
种子休眠
植物
发芽
休眠
生物
作者
Xiaolong Yang,Hui Meng Chang
标识
DOI:10.1016/j.slast.2024.100177
摘要
Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a crucial role in patient prevention and treatment. The study aimed to establish and validate a risk stratification model for stroke within three years in patients with CSVD using a combined MRI and machine learning algorithm approach.
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