Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis.

ABSTRACT:

Background

The Society of Thoracic Surgeons and European System for Cardiac Operative Risk Evaluation (EuroSCORE) II risk scores are the most commonly used risk prediction models for in-hospital mortality after adult cardiac surgery. However, they are prone to miscalibration over time and poor generalization across data sets; thus, their use remains controversial. Despite increased interest, a gap in understanding the effect of data set drift on the performance of machine learning (ML) over time remains a barrier to its wider use in clinical practice. Data set drift occurs when an ML system underperforms because of a mismatch between the data it was developed from and the data on which it is deployed.

Objective

In this study, we analyzed the extent of performance drift using

SUBMITTER: Dong T 

PROVIDER: S-EPMC11217160 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature