AIMC Topic: Intensive Care Units, Pediatric

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Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.

Scientific reports Jan 13, 2021
The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hosp...
Humans Machine Learning Retrospective Studies Female Infant Mortality Patient Admission Male Infant Child, Preschool Child Hospital Mortality Intensive Care Units, Pediatric Models, Biological Child Mortality
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A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare.

BMC medical informatics and decision making Oct 8, 2020
BACKGROUND: There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, approaches to explaining complex machin...
Humans Machine Learning Health Personnel Delivery of Health Care Focus Groups Qualitative Research Child Hospital Mortality Intensive Care Units, Pediatric
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Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.

Journal of clinical monitoring and computing Sep 27, 2018
A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients' physiolog...
Critical Care Systole Respiratory Rate Heart Arrest Infant Child, Preschool Child Intensive Care Units, Pediatric Oxygen Signal Processing, Computer-Assisted Heart Rate Monitoring, Physiologic Blood Pressure Critical Illness Infant, Newborn Reproducibility of Results Sensitivity and Specificity Humans Machine Learning Software
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The Dependence of Machine Learning on Electronic Medical Record Quality.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work investigated the impact of this disparity on the performance of three a...
Infant Humans Los Angeles Coronary Care Units Hospital Mortality Adolescent Data Accuracy Machine Learning Intensive Care Units, Pediatric Child, Preschool Algorithms Logistic Models Child Electronic Health Records Hospitals, Pediatric
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An ensemble boosting model for predicting transfer to the pediatric intensive care unit.

International journal of medical informatics Jan 9, 2018
BACKGROUND: Early deterioration indicators have the potential to alert hospital care staff in advance of adverse events, such as patients requiring an increased level of care, or the need for rapid response teams to be called. Our work focuses on the...
Child, Hospitalized Female Male Severity of Illness Index Artificial Intelligence Child Models, Statistical Machine Learning Health Services Needs and Demand Intensive Care Units, Pediatric Algorithms Humans Patient Transfer Retrospective Studies ROC Curve
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Interpretable Deep Models for ICU Outcome Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns of...
Intensive Care Units, Pediatric Humans Models, Theoretical Computer Simulation Acute Lung Injury Prognosis Neural Networks, Computer Electronic Health Records Machine Learning
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Interhospital Transfer of Children in Septic Shock: A Clinician Interview Qualitative Study.

Journal of intensive care medicine Dec 14, 2016
OBJECTIVE:: To determine the factors that influence the decision to transfer children in septic shock from level II to level I pediatric intensive care unit (PICU) care.
Humans Renal Replacement Therapy Intensive Care Units, Pediatric Extracorporeal Membrane Oxygenation Shock, Septic Combined Modality Therapy Catheterization, Central Venous Vasoconstrictor Agents Fluid Therapy Patient Transfer Clinical Decision-Making Male Qualitative Research Adolescent
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The predictive value of soluble endothelial selectin plasma levels in children with acute lung injury.

Journal of critical care Dec 23, 2015
UNLABELLED: The study aimed to evaluate the value of soluble endothelial selectin (sE-selectin) plasma level measurement in predicting acute lung injury (ALI) outcome in children.
Egypt Acute Lung Injury Selectins Child Intensive Care Units, Pediatric Predictive Value of Tests C-Reactive Protein Male Humans Biomarkers Respiration, Artificial Prospective Studies Prognosis Adolescent Female Child, Preschool
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Heart Rate and Body Temperature Relationship in Children Admitted to PICU: A Machine Learning Approach.

IEEE transactions on bio-medical engineering Aug 1, 2025
UNLABELLED: Vital signs are crucial clinical measures, with body temperature (BT) and heart rate (HR) being particularly significant. While their association has been studied in adults and children, research in Pediatric Intensive Care Unit (PICU) se...
Humans Child Machine Learning Signal Processing, Computer-Assisted Male Female Intensive Care Units, Pediatric Heart Rate Infant Infant, Newborn Body Temperature Adolescent Child, Preschool
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Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

Hospital pediatrics Jun 1, 2025
OBJECTIVE: The medical community recently experienced a severe shortage of blood culture media bottles. Rates of blood stream infection (BSI) among critically ill children are low. We sought to design a machine learning (ML) model able to identify ch...
Blood Culture Child, Preschool Child Intensive Care Units, Pediatric Bacteremia Humans Retrospective Studies Male Machine Learning Female Infant Adolescent
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