A Predictive Model for Post-Percutaneous Coronary Intervention Readmission: Insights from Machine Learning on Clinical Risk Factors.
Introduction: Unplanned one-year readmission after percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI) poses serious clinical and economic challenges. This study developed and validated a random forest (RF) model to predict one-year all-cause unplanned rea...
| Published in: | Journal of Research in Health Sciences Vol. 26; no. 3; pp. 1 - 10 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Hamadan University of Medical Sciences, School of Public Health
Summer2026
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| Online Access: | View this record in EBSCOhost |