格拉斯哥昏迷指数
置信区间
判别式
医学
冲程(发动机)
机器学习
比例危险模型
肺炎
接收机工作特性
随机森林
人工智能
统计
计算机科学
内科学
外科
数学
机械工程
工程类
作者
Chang-Ching Lee,Sheng‐You Su,Sheng‐Feng Sung
标识
DOI:10.1016/j.ijmedinf.2024.105422
摘要
Post-stroke pneumonia (PSP) is common among stroke patients. PSP occurring after hospital discharge continues to increase the risk of poor functional outcomes and death among stroke survivors. Currently, there is no prediction model specifically designed to predict the occurrence of PSP beyond the acute stage of stroke. This study aimed to explore the use of machine learning (ML) methods in predicting the risk of PSP after hospital discharge.
科研通智能强力驱动
Strongly Powered by AbleSci AI