AIMC Topic: Intensive Care Units, Pediatric

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Ethical Considerations Related to Using Machine Learning-Based Prediction of Mortality in the Pediatric Intensive Care Unit.

The Journal of pediatrics Jan 14, 2022
Machine Learning Intensive Care Units Child Intensive Care Units, Pediatric Humans
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Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.

Critical care (London, England) Aug 10, 2021
BACKGROUND: Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it...
Child, Preschool Humans Machine Learning Child Computer Simulation Intensive Care Units, Pediatric Acute Kidney Injury ROC Curve Infant Female Severity of Illness Index Infant, Newborn Adolescent Young Adult Cohort Studies Male Area Under Curve Critical Care Pediatrics
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Development and validation of a deep-learning-based pediatric early warning system: A single-center study.

Biomedical journal Jan 18, 2021
BACKGROUND: Early detection and prompt intervention for clinically deteriorating events are needed to improve clinical outcomes. There have been several attempts at this, including the introduction of rapid response teams (RRTs) with early warning sc...
Retrospective Studies Deep Learning Child Intensive Care Units, Pediatric Humans ROC Curve Heart Arrest
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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...
Retrospective Studies Female Male Infant Humans Machine Learning Models, Biological Child Mortality Infant Mortality Patient Admission Child, Preschool Child Hospital Mortality Intensive Care Units, Pediatric
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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...
Hospital Mortality Intensive Care Units, Pediatric Focus Groups Humans Machine Learning Health Personnel Delivery of Health Care Qualitative Research Child
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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...
Respiratory Rate Heart Arrest Signal Processing, Computer-Assisted Heart Rate Critical Care Systole Intensive Care Units, Pediatric Software Monitoring, Physiologic Critical Illness Blood Pressure Infant, Newborn Oxygen Reproducibility of Results Sensitivity and Specificity Infant Child, Preschool Child Humans Machine Learning
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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...
Child Hospital Mortality Intensive Care Units, Pediatric Logistic Models Hospitals, Pediatric Los Angeles Humans Data Accuracy Machine Learning Electronic Health Records Coronary Care Units Algorithms Infant Adolescent Child, Preschool
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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...
Artificial Intelligence Machine Learning Algorithms Retrospective Studies Female Male Severity of Illness Index Child Humans Patient Transfer Child, Hospitalized Intensive Care Units, Pediatric ROC Curve Models, Statistical Health Services Needs and Demand
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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 Neural Networks, Computer Acute Lung Injury Machine Learning Computer Simulation Prognosis Electronic Health Records
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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.
Qualitative Research Adolescent Intensive Care Units, Pediatric Shock, Septic Humans Renal Replacement Therapy Clinical Decision-Making Male Vasoconstrictor Agents Patient Transfer Catheterization, Central Venous Fluid Therapy Extracorporeal Membrane Oxygenation Combined Modality Therapy
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