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Intensive Care Units, Pediatric

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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...
Adolescent Algorithms Child Child, Preschool Coronary Care Units Data Accuracy Electronic Health Records Hospital Mortality Hospitals, Pediatric Humans Infant Intensive Care Units, Pediatric Logistic Models Los Angeles 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.
Adolescent Catheterization, Central Venous Clinical Decision-Making Combined Modality Therapy Extracorporeal Membrane Oxygenation Fluid Therapy Humans Intensive Care Units, Pediatric Male Patient Transfer Qualitative Research Renal Replacement Therapy Shock, Septic Vasoconstrictor Agents
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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 ...
Algorithms Child Critical Care Data Collection Decision Support Systems, Clinical Humans Intensive Care Units, Pediatric Machine Learning Pediatrics
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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...
Critical Care Electronic Health Records Information Dissemination Intensive Care Units, Pediatric Machine Learning
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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 Aug 1, 2019
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...
Blood Pressure Child Child, Preschool Critical Care Critical Illness Heart Arrest Heart Rate Humans Infant Infant, Newborn Intensive Care Units, Pediatric Machine Learning Monitoring, Physiologic Oxygen Reproducibility of Results Respiratory Rate Sensitivity and Specificity Signal Processing, Computer-Assisted Software Systole
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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
Artificial Intelligence Child Humans Intensive Care Units, Pediatric Sepsis
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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.
Adolescent Artificial Intelligence Case-Control Studies Child Female Heart Rate Humans Intensive Care Units, Pediatric Logistic Models Machine Learning Male Monitoring, Physiologic Organ Dysfunction Scores Predictive Value of Tests Prospective Studies Respiratory Rate Sepsis
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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.
Adolescent Child Child, Preschool Humans Infant Intensive Care Units, Pediatric Machine Learning Neural Networks, Computer Patient Discharge Reference Values Regression Analysis Vital Signs
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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
Artificial Intelligence Child Humans Intensive Care Units, Pediatric Sepsis
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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
Airway Management Algorithms Anesthesia Artificial Intelligence Biomedical Research Blood Transfusion Critical Care Heart Arrest Hemorrhage Hospital Mortality Humans Intensive Care Units, Pediatric Intraoperative Complications Machine Learning Monitoring, Intraoperative Patient Admission Perioperative Care Postoperative Complications Risk Assessment Ultrasonography
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