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

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...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser: 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
Format: Artikel
Sprache:English
Veröffentlicht: Elsevier 2024-02-01
Schriftenreihe:Heliyon
Schlagworte:
Congenital heart disease
ICU-LOS prediction
Machine learning
PyCaret library
Light gradient boosting machine
Congenital heart surgery
Online Zugang:http://www.sciencedirect.com/science/article/pii/S2405844024014373

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

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