AIMC Topic: Intensive Care Units, Pediatric
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Exploring Heterogeneity in Cost-Effectiveness Using Machine Learning Methods: A Case Study Using the FIRST-ABC Trial.
Medical care
Jun 7, 2024
OBJECTIVE: The aim of this study was to explore heterogeneity in the cost-effectiveness of high-flow nasal cannula (HFNC) therapy compared with continuous positive airway pressure (CPAP) in children following extubation.
Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights.
Respiratory research
May 23, 2024
The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pre...
Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.
The Pediatric infectious disease journal
May 8, 2024
BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sep...
A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.
PloS one
May 1, 2024
Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-le...
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.
Computer methods and programs in biomedicine
Apr 10, 2024
BACKGROUND AND OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Underg...
Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit.
Pediatric pulmonology
Feb 14, 2024
OBJECTIVES: This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making.
Comparing the Quality of Domain-Specific Versus General Language Models for Artificial Intelligence-Generated Differential Diagnoses in PICU Patients.
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
Feb 8, 2024
OBJECTIVES: Generative language models (LMs) are being evaluated in a variety of tasks in healthcare, but pediatric critical care studies are scant. Our objective was to evaluate the utility of generative LMs in the pediatric critical care setting an...
Predicting long-term neurocognitive outcome after pediatric intensive care unit admission for bronchiolitis-preliminary exploration of the potential of machine learning.
European journal of pediatrics
Nov 6, 2023
PURPOSE: For successful prevention and intervention, it is important to unravel the complex constellation of factors that affect neurocognitive functioning after pediatric intensive care unit (PICU) admission. This study aims (1) to elucidate the pot...
Development of a deep learning model that predicts Bi-level positive airway pressure failure.
Scientific reports
May 26, 2022
Delaying intubation for patients failing Bi-Level Positive Airway Pressure (BIPAP) may be associated with harm. The objective of this study was to develop a deep learning model capable of aiding clinical decision making by predicting Bi-Level Positiv...
Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
Apr 21, 2022
OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithm...
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