Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to PICUs betwe...

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Bibliographic Details
Main Authors: Rosanna I. Comoretto, Danila Azzolina, Angela Amigoni, Giorgia Stoppa, Federica Todino, Andrea Wolfler, Dario Gregori, on behalf of the TIPNet Study Group
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Diagnostics
Subjects:
machine learning techniques
hemodynamic failure
PICU
imbalance management
outcome prediction
Online Access:https://www.mdpi.com/2075-4418/11/7/1299

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