AIMC Topic: Intensive Care Units, Pediatric

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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
Sepsis Child Intensive Care Units, Pediatric Humans Artificial Intelligence
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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.
Reference Values Vital Signs Infant Regression Analysis Patient Discharge Adolescent Child Intensive Care Units, Pediatric Child, Preschool Neural Networks, Computer Machine Learning Humans
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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
Intensive Care Units, Pediatric Humans Sepsis Child Artificial Intelligence
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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.
Child Intensive Care Units, Pediatric Sepsis Adolescent Predictive Value of Tests Heart Rate Organ Dysfunction Scores Logistic Models Monitoring, Physiologic Prospective Studies Female Male Case-Control Studies Artificial Intelligence Machine Learning Humans Respiratory Rate
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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 Machine Learning Intensive Care Units, Pediatric Electronic Health Records Critical Care
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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 ...
Intensive Care Units, Pediatric Decision Support Systems, Clinical Critical Care Pediatrics Data Collection Algorithms Child Humans Machine Learning
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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.
Support Vector Machine Decision Trees Intensive Care Units, Pediatric Infant ROC Curve Retrospective Studies Child, Preschool Adolescent Linear Models Young Adult Predictive Value of Tests Child Multivariate Analysis Area Under Curve Humans Heart Arrest Neural Networks, Computer
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