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Risk factors for mechanical ventilation and reintubation after pediatric heart surgery

The Journal of thoracic and cardiovascular surgery, 2016-02, Vol.151 (2), p.451-458.e3

2016

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First Get the Data, Then Do the Science

Pediatric critical care medicine, 2018-04, Vol.19 (4), p.382-383

Wetzel, Randall C

2018

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Applying Machine Learning to Pediatric Critical Care Data

Pediatric critical care medicine, 2018-07, Vol.19 (7), p.599-608

2018

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Impact of 24/7 In-Hospital Intensivist Coverage on Outcomes in Pediatric Intensive Care. A Multicenter Study

American journal of respiratory and critical care medicine, 2016-12, Vol.194 (12), p.1506-1513

2016

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Narrowing the gap: expected versus deployment performance

Journal of the American Medical Informatics Association : JAMIA, 2023-08, Vol.30 (9), p.1474-1485

2023

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Pediatric Intensive Care Databases for Quality Improvement

Journal of pediatric intensive care, 2016-09, Vol.5 (3), p.081-088

Wetzel, Randall C.

2016

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The Impact of Coronavirus Disease 2019 Pandemic on U.S. and Canadian PICUs

Pediatric critical care medicine, 2020-09, Vol.21 (9), p.e643

2020

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Epidemiology and outcomes of in-hospital cardiac arrest in critically ill children across hospitals of varied center volume: A multi-center analysis

Resuscitation, 2014-11, Vol.85 (11), p.1473-1479

2014

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Development of a deep learning model that predicts Bi-level positive airway pressure failure

Scientific reports, 2022-05, Vol.12 (1), p.8907-8907, Article 8907

2022

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Obesity and Mortality Risk in Critically Ill Children

Pediatrics (Evanston), 2016-03, Vol.137 (3), p.e20152035

2016

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Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients

Critical care medicine, 2018-01, Vol.46 (1), p.108-115

2018

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Association of Freestanding Children’s Hospitals With Outcomes in Children With Critical Illness

Critical care medicine, 2016-12, Vol.44 (12), p.2131-2138

2016

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Signaling networks assembled by oncogenic EGFR and c-Met

Proceedings of the National Academy of Sciences - PNAS, 2008-01, Vol.105 (2), p.692-697

2008

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Artificial Intelligence: An Inkling of Caution

Pediatric critical care medicine, 2018-10, Vol.19 (10), p.1004-1005

2018

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Comparison of high-frequency oscillatory ventilation and conventional mechanical ventilation in pediatric respiratory failure

JAMA pediatrics, 2014-03, Vol.168 (3), p.243

2014

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Machine Learning to Predict Cardiac Death Within 1 Hour After Terminal Extubation

Pediatric critical care medicine, 2021-02, Vol.22 (2), p.161

2021

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Effect of Inhaled Nitric Oxide on Outcomes in Children With Acute Lung Injury: Propensity Matched Analysis From a Linked Database

Critical care medicine, 2016-10, Vol.44 (10), p.1901-1909

2016

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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, 2018-12, Vol.25 (12), p.1600-1607

2018

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Predicting High Flow Nasal Cannula Failure in an Intensive Care Unit Using a Recurrent Neural Network With Transfer Learning and Input Data Perseveration: Retrospective Analysis

JMIR medical informatics, 2022-03, Vol.10 (3), p.e31760-e31760

2022

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Risk factors and outcomes of in-hospital cardiac arrest following pediatric heart operations of varying complexity

Resuscitation, 2016-08, Vol.105, p.1-7

2016

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Prehospitalization Trauma and Physiologic Factors Associated with the Presence of Post-traumatic Stress 3 Months After PICU Discharge

Journal of intensive care medicine, 2024-03, Vol.39 (3), p.268-276

2024

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Are all ICUs the same?

Pediatric anesthesia, 2011-07, Vol.21 (7), p.787-793

2011

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Improving Recurrent Neural Network Responsiveness to Acute Clinical Events

IEEE access, 2021, Vol.9, p.106140-106151

2021

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Risk factors for mechanical ventilation and reintubation after pediatric heart surgery

First Get the Data, Then Do the Science

Applying Machine Learning to Pediatric Critical Care Data

Impact of 24/7 In-Hospital Intensivist Coverage on Outcomes in Pediatric Intensive Care. A Multicenter Study

Narrowing the gap: expected versus deployment performance

Pediatric Intensive Care Databases for Quality Improvement

The Impact of Coronavirus Disease 2019 Pandemic on U.S. and Canadian PICUs

Epidemiology and outcomes of in-hospital cardiac arrest in critically ill children across hospitals of varied center volume: A multi-center analysis

Development of a deep learning model that predicts Bi-level positive airway pressure failure

Obesity and Mortality Risk in Critically Ill Children

Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients

Association of Freestanding Children’s Hospitals With Outcomes in Children With Critical Illness

Signaling networks assembled by oncogenic EGFR and c-Met

Artificial Intelligence: An Inkling of Caution

Comparison of high-frequency oscillatory ventilation and conventional mechanical ventilation in pediatric respiratory failure

Machine Learning to Predict Cardiac Death Within 1 Hour After Terminal Extubation

Effect of Inhaled Nitric Oxide on Outcomes in Children With Acute Lung Injury: Propensity Matched Analysis From a Linked Database

Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit

Ventilator Weaning and Terminal Extubation: Withdrawal of Life-Sustaining Therapy in Children. Secondary Analysis of the Death One Hour After Terminal Extubation Study

Predicting High Flow Nasal Cannula Failure in an Intensive Care Unit Using a Recurrent Neural Network With Transfer Learning and Input Data Perseveration: Retrospective Analysis

Risk factors and outcomes of in-hospital cardiac arrest following pediatric heart operations of varying complexity

Prehospitalization Trauma and Physiologic Factors Associated with the Presence of Post-traumatic Stress 3 Months After PICU Discharge

Repertoires of SARS-CoV-2 epitopes targeted by antibodies vary according to severity of COVID-19

Are all ICUs the same?

Improving Recurrent Neural Network Responsiveness to Acute Clinical Events

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