Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients: Short title: Prediction of HFpEF readmission.

Journal: International journal of medical informatics

PMID: 39571389

Abstract

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is associated with elevated rates of readmission and mortality. Accurate prediction of readmission risk is essential for optimizing healthcare resources and enhancing patient outcomes.

Authors

Yue Hu

Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China.
Fanghui Ma

Department of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Mengjie Hu
Binbing Shi

Department of General Practice, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
Defeng Pan

Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
Jingjing Ren

Keywords

Aged Female Heart Failure Humans Machine Learning Male Middle Aged Patient Readmission Retrospective Studies Risk Assessment Risk Factors ROC Curve Stroke Volume

External Resources

View on PubMed Access via DOI PubMed (39571389)

Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients: Short title: Prediction of HFpEF readmission.

Abstract

Authors

Keywords

External Resources

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