AIMC Topic: Intensive Care Units, Pediatric

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Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategie...
Humans Child Neural Networks, Computer Prognosis Intensive Care Units, Pediatric Heart Rate Sepsis Early Diagnosis
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Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care.

The Journal of pediatrics Jun 1, 2020
Humans Artificial Intelligence Machine Learning Airway Management Perioperative Care Patient Admission Intraoperative Complications Heart Arrest Critical Care Hemorrhage Anesthesia Hospital Mortality Algorithms Postoperative Complications Intensive Care Units, Pediatric Biomedical Research Blood Transfusion Risk Assessment Monitoring, Intraoperative Ultrasonography
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Artificial Intelligence: Progress Towards an Intelligent Clinical Support System.

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 1, 2019
Humans Artificial Intelligence Sepsis Child Intensive Care Units, Pediatric
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Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2018
OBJECTIVE: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.
Vital Signs Regression Analysis Patient Discharge Reference Values Machine Learning Infant Adolescent Child, Preschool Child Intensive Care Units, Pediatric Humans Neural Networks, Computer
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Artificial Intelligence: An Inkling of Caution.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Oct 1, 2018
Humans Sepsis Artificial Intelligence Child Intensive Care Units, Pediatric
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Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in 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 Oct 1, 2018
OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children.
Respiratory Rate Case-Control Studies Predictive Value of Tests Sepsis Adolescent Heart Rate Prospective Studies Organ Dysfunction Scores Logistic Models Female Humans Monitoring, Physiologic Child Male Artificial Intelligence Intensive Care Units, Pediatric Machine Learning
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Applying Machine Learning to Pediatric Critical Care Data.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Jul 1, 2018
OBJECTIVES: To explore whether machine learning applied to pediatric critical care data could discover medically pertinent information, we analyzed clinically collected electronic medical record data, after data extraction and preparation, using k-me...
Information Dissemination Electronic Health Records Intensive Care Units, Pediatric Critical Care Machine Learning
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Learning Healthcare Systems in Pediatrics: Cross-Institutional and Data-Driven Decision-Support for Intensive Care Environments (CADDIE).

Studies in health technology and informatics Jan 1, 2018
BACKGROUND: The vast amount of data generated in healthcare can be reused to support decision-making by developing clinical decision-support systems. Since evidence is lacking in Pediatrics, it seems to be beneficial to design future systems towards ...
Humans Intensive Care Units, Pediatric Decision Support Systems, Clinical Machine Learning Critical Care Data Collection Algorithms Pediatrics Child
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Using Time Series Analysis to Predict Cardiac Arrest in a 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 Nov 1, 2015
OBJECTIVES: To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data.
Multivariate Analysis Heart Arrest Predictive Value of Tests Area Under Curve Linear Models Child, Preschool Humans Child Young Adult ROC Curve Neural Networks, Computer Infant Intensive Care Units, Pediatric Support Vector Machine Retrospective Studies Adolescent Decision Trees
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