Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.

ABSTRACT:

Aims

Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.

Methods and results

This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines. PubMed, EMBASE, Web of Science, and Cochrane databases were searched until 1 November 2023 for studies c

SUBMITTER: Zaka A 

PROVIDER: S-EPMC11750198 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature