Article

Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects

2024-09-10

Abstract excerpt

<h4>Background: </h4> Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, are limited. <h4>Objectives:</h4> The aim of this work is to develop a binary outcome mixed effects Xgboost (BME) model that integrates random effects at the hospit...

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Literature Corpus work: 67c369c6-e0f9-5196-a952-e1db697d5612
DOI: 10.20944/preprints202409.0698.v1

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