作者
Rohan Khera, Julian Haimovich, Nathan C Hurley, Robert McNamara, John A Spertus, Nihar Desai, John S Rumsfeld, Frederick A Masoudi, Chenxi Huang, Sharon-Lise Normand, Bobak J Mortazavi, Harlan M Krumholz
发表日期
2021/6/1
期刊
JAMA cardiology
卷号
6
期号
6
页码范围
633-641
出版商
American Medical Association
简介
Importance
Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.
Objective
To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.
Design, Setting, and Participants
This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.
Main Outcomes and Measures
Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation …
学术搜索中的文章
R Khera, J Haimovich, NC Hurley, R McNamara… - JAMA cardiology, 2021