Accuracy of machine learning in predicting outcomes post-percutaneous coronary intervention: a systematic review.

ABSTRACT:

Background

Recent studies have shown potential in introducing machine learning (ML) algorithms to predict outcomes post-percutaneous coronary intervention (PCI).

Aims

We aimed to critically appraise current ML models' effectiveness as clinical tools to predict outcomes post-PCI.

Methods

Searches of four databases were conducted for articles published from the database inception date to 29 May 2021. Studies using ML to predict outcomes post-PCI were included. For individual post-PCI outcomes, measures of diagnostic accuracy were extracted. An adapted checklist comprising existing frameworks for new risk markers, diagnostic accuracy, prognostic tools and ML was used to critically appraise the included studies along the stages of the translational pathway: development, v

SUBMITTER: Wee CF 

PROVIDER: S-EPMC11413637 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature