医学
慢性阻塞性肺疾病急性加重期
恶化
重症监护医学
肺病
机器学习
疾病
人工智能
梅德林
急诊医学
疾病严重程度
慢性阻塞性肺病
风险评估
预测建模
组学
临床实习
分级(工程)
支持向量机
物理疗法
试验预测值
内科学
作者
Zian Liu,Shiyuan Gao,Zhe Ye,Qiong Pan,Yiwen Huang,Jiahui Yuan,Fengmei Li,Yixin Lian,Chen Geng
标识
DOI:10.1186/s40001-025-03601-4
摘要
The disease grading prediction model of AECOPD inpatients constructed based on clinical data and muscle imaging omics characteristics has good performance, and has great potential in assisting clinicians to more accurately stratify the risk of AECOPD inpatients.
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