AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Intensive Care Units, Pediatric

Showing 1 to 10 of 44 articles

Clear Filters

Plasma IL-17A is increased in patients with critical MIS-C and associated to in-hospital mortality.

Frontiers in immunology 39931580
BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe post-COVID-19 complication with multiple phenotypes.
Adolescent Biomarkers Child Child, Preschool COVID-19 Female Hospital Mortality Humans Infant Infant, Newborn Intensive Care Units, Pediatric Interleukin-17 Male Oxidative Stress Prospective Studies ROC Curve SARS-CoV-2 Systemic Inflammatory Response Syndrome
View on PubMed DOI

Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights.

Respiratory research 38783298
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...
Adolescent Child Child, Preschool Databases, Factual Female Hospital Mortality Humans Infant Infant, Newborn Intensive Care Units, Pediatric Machine Learning Male Predictive Value of Tests Respiratory Tract Diseases
View on PubMed DOI

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

The Pediatric infectious disease journal 38717173
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...
Adolescent Artificial Intelligence Child Child, Preschool Female Humans Infant Infant, Newborn Intensive Care Units, Pediatric Male Neural Networks, Computer Retrospective Studies Sepsis Support Vector Machine
View on PubMed DOI

Exploring Heterogeneity in Cost-Effectiveness Using Machine Learning Methods: A Case Study Using the FIRST-ABC Trial.

Medical care 38848138
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.
Cannula Child Child, Preschool Continuous Positive Airway Pressure Cost-Benefit Analysis Female Humans Infant Intensive Care Units, Pediatric Machine Learning Male Oxygen Inhalation Therapy Quality-Adjusted Life Years United Kingdom
View on PubMed DOI

Exploring Heterogeneity in the Cost-Effectiveness of High-Flow Nasal Cannula Therapy in Acutely Ill Children-Insights From the Step-Up First-line Support for Assistance in Breathing in Children Trial Using a Machine Learning Method.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research 39349099
OBJECTIVES: To investigate heterogeneity in the cost-effectiveness of high-flow nasal cannula (HFNC) therapy compared with continuous positive airway pressure (CPAP) for acutely ill children requiring noninvasive respiratory support.
Acute Disease Adolescent Cannula Child Child, Preschool Continuous Positive Airway Pressure Cost-Benefit Analysis Equivalence Trials as Topic Female Humans Infant Infant, Newborn Intensive Care Units, Pediatric Machine Learning Male Multicenter Studies as Topic Oxygen Inhalation Therapy Quality-Adjusted Life Years Randomized Controlled Trials as Topic United Kingdom
View on PubMed DOI

From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.

Intensive care medicine 39264415
PURPOSE: Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benef...
Artificial Intelligence Child Critical Illness Humans Infant, Newborn Intensive Care Units, Neonatal Intensive Care Units, Pediatric
View on PubMed DOI

PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

Shock (Augusta, Ga.) 39671551
Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteri...
Adolescent Child Child, Preschool Female Hospital Mortality Humans Infant Intensive Care Units, Pediatric Machine Learning Male Prognosis Retrospective Studies Sepsis Shock, Septic
View on PubMed DOI

Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 39982152
OBJECTIVES: To describe the deployment of pediatric Calculated Assessment of Risk and Triage (pCART), a machine learning (ML) model to predict the risk of the direct ward to the ICU transfer within 12 hours, and the associated improved outcomes among...
Adolescent Child Child, Preschool Early Warning Score Female Hospital Mortality Humans Infant Infant, Newborn Intensive Care Units, Pediatric Machine Learning Male Patient Transfer Respiration, Artificial Risk Assessment Triage
View on PubMed DOI

Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

JAMA network open 39903466
IMPORTANCE: Decreasing mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children...
Adolescent Biomarkers Child Child, Preschool Critical Illness Female Humans Infant Intensive Care Units, Pediatric Machine Learning Male Nervous System Diseases Prognosis Risk Assessment
View on PubMed DOI

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 40047501
OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using serum biomarkers can be determined solely from electronic health record (EHR) data using machine learn...
Adolescent Biomarkers Child Child, Preschool Electronic Health Records Female Humans Infant Intensive Care Units, Pediatric Machine Learning Male Phenotype Respiratory Distress Syndrome Retrospective Studies ROC Curve
View on PubMed DOI
Popular Topics
Recent Journals