Predictors of in-ICU length of stay among congenital heart defect patients using artificial intelligence model: A pilot study

Objective: This study aims to develop a predictive model using artificial intelligence to estimate the ICU length of stay (LOS) for Congenital Heart Defects (CHD) patients after surgery, improving care planning and resource management. Design: We analyze clinical data from 2240 CHD surgery patients...

תיאור מלא

מידע ביבליוגרפי
Main Authors:	João Chang Junior, Luiz Fernando Caneo, Aida Luiza Ribeiro Turquetto, Luciana Patrick Amato, Elisandra Cristina Trevisan Calvo Arita, Alfredo Manoel da Silva Fernandes, Evelinda Marramon Trindade, Fábio Biscegli Jatene, Paul-Eric Dossou, Marcelo Biscegli Jatene
פורמט:	Article
שפה:	English
יצא לאור:	Elsevier 2024-02-01
סדרה:	Heliyon
נושאים:	Congenital heart disease ICU-LOS prediction Machine learning PyCaret library Light gradient boosting machine Congenital heart surgery
גישה מקוונת:	http://www.sciencedirect.com/science/article/pii/S2405844024014373

אינטרנט

http://www.sciencedirect.com/science/article/pii/S2405844024014373

Predictors of in-ICU length of stay among congenital heart defect patients using artificial intelligence model: A pilot study

אינטרנט

פריטים דומים