Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.

Journal: BMC cardiovascular disorders

PMID: 40108540

Abstract

BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), and to visualize the model results in order to analyze the impact of these predictors on the patients' prognosis.

Authors

Caiyu Shen

School of Health Management, Bengbu Medical University, Bengbu, Anhui, 233030, China.
Shuai Wang

Department of Intensive Care Unit, China-Japan Union Hospital of Jilin University, Changchun, China.
Ruiheng Huo

School of Health Management, Bengbu Medical University, Bengbu, Anhui, 233030, China.
Yuli Huang

Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, 233004, China. 1310388842@qq.com.
Shu Yang

Department of Health Management, Bengbu Medical College, Bengbu, 233030.

Keywords

Aged Databases, Factual Decision Support Techniques Female Hospital Mortality Humans Machine Learning Male Middle Aged Myocardial Infarction Nomograms Predictive Value of Tests Prognosis Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors Shock, Cardiogenic Time Factors

External Resources

View on PubMed Access via DOI PubMed (40108540)

Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.

Abstract

Authors

Keywords

External Resources

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