医学
逻辑回归
机器学习
人工智能
梯度升压
心肌梗塞
急诊分诊台
内科学
胸痛
Boosting(机器学习)
急诊医学
计算机科学
随机森林
作者
Rohan Khera,Julian S. Haimovich,Nathan C. Hurley,Robert L. McNamara,John A. Spertus,Nihar R. Desai,John S. Rumsfeld,Frederick A. Masoudi,Chenxi Huang,Sharon‐Lise T. Normand,Bobak J. Mortazavi,Harlan M. Krumholz
出处
期刊:JAMA Cardiology
[American Medical Association]
日期:2021-03-10
卷期号:6 (6): 633-633
被引量:246
标识
DOI:10.1001/jamacardio.2021.0122
摘要
In this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.
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