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

Background: 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, is limited. Objectives: The aim of this work is to develo...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Tim Dong, Iyabosola Busola Oronti, Shubhra Sinha, Alberto Freitas, Bing Zhai, Jeremy Chan, Daniel P. Fudulu, Massimo Caputo, Gianni D. Angelini
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2024-10-01
Sarja:	Bioengineering
Aiheet:	machine learning AI random effects cardiovascular medicine risk prediction expectation–maximization
Linkit:	https://www.mdpi.com/2306-5354/11/10/1039
Tagit:	Lisää tagi Ei tageja, Lisää ensimmäinen tagi!