AIMC Topic: Critical Care

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The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care.

Nature medicine Oct 22, 2018
Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids ...
Humans Artificial Intelligence Sepsis Cohort Studies Vasoconstrictor Agents Critical Care Administration, Intravenous Software Clinical Decision-Making Learning Female Male
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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...
Systole Respiratory Rate Heart Arrest Oxygen Signal Processing, Computer-Assisted Heart Rate Critical Care Humans Machine Learning Software Critical Illness Child, Preschool Child Intensive Care Units, Pediatric Infant, Newborn Monitoring, Physiologic Reproducibility of Results Blood Pressure Sensitivity and Specificity Infant
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Big data and machine learning in critical care: Opportunities for collaborative research.

Medicina intensiva Aug 2, 2018
The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well...
Humans Machine Learning Databases, Factual Interdisciplinary Research Critical Illness Big Data Critical Care Spain
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Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.

Journal of clinical monitoring and computing May 24, 2018
Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this stu...
Bayes Theorem Signal Processing, Computer-Assisted Hypotension Critical Care False Positive Reactions Software Adolescent Databases, Factual Male Brain Injuries, Traumatic Prospective Studies Middle Aged Intensive Care Units Brain Injuries Diagnosis, Computer-Assisted Female Area Under Curve Pilot Projects Humans Sample Size Aged Neural Networks, Computer Aged, 80 and over Machine Learning Young Adult Adult Sensitivity and Specificity
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Supervised machine learning in critical care: The path forward.

Journal of critical care May 18, 2018
Supervised Machine Learning Humans Critical Care Bacteremia
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Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.

Journal of child neurology May 13, 2018
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine ...
Infant Diagnosis, Computer-Assisted Adolescent Child, Preschool Child Infant, Newborn Young Adult Sensitivity and Specificity Male Critical Care Neurophysiological Monitoring Hospital Mortality Area Under Curve Electroencephalography Epilepsy Seizures Humans Machine Learning Prognosis Female
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Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement.

The Journal of surgical research Apr 11, 2018
BACKGROUND: Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostom...
Risk Assessment Respiration, Artificial Severity of Illness Index Length of Stay Time Factors Treatment Outcome Feasibility Studies Supervised Machine Learning Patient Selection Humans Decision Support Techniques Databases, Factual Critical Illness Intensive Care Units Critical Care Tracheostomy
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A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram.

Journal of healthcare engineering Mar 7, 2018
The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) usi...
Databases, Factual Female Critical Care Reproducibility of Results Aged Pattern Recognition, Automated Photoplethysmography Young Adult Humans Systole Aged, 80 and over Electrocardiography Adult Neural Networks, Computer Diastole Male Blood Pressure Determination Middle Aged Blood Pressure Hypertension Pulse Wave Analysis
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What's new in ICU in 2050: big data and machine learning.

Intensive care medicine Dec 26, 2017
Big Data Humans Critical Care Machine Learning Forecasting Precision Medicine
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High-Dose Vitamin D Administration Is Associated With Increases in Hemoglobin Concentrations in Mechanically Ventilated Critically Ill Adults: A Pilot Double-Blind, Randomized, Placebo-Controlled Trial.

JPEN. Journal of parenteral and enteral nutrition Dec 11, 2017
BACKGROUND: Anemia and vitamin D deficiency are highly prevalent in critical illness, and vitamin D status has been associated with hemoglobin concentrations in epidemiologic studies. We examined the effect of high-dose vitamin D therapy on hemoglobi...
Cholecalciferol Vitamins Humans Male Hepcidins Critical Illness Middle Aged Hemoglobins Respiration, Artificial Female Critical Care Pilot Projects Aged Double-Blind Method
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