AIMC Topic: Heart Arrest

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Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

PLoS medicine Nov 30, 2018
BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admi...
Retrospective Studies Clinical Decision-Making Female Risk Factors Aged Male Humans Machine Learning Decision Support Techniques Databases, Factual Middle Aged Risk Assessment Time Factors Hospital Mortality Treatment Outcome Health Status Australia Registries New Zealand Cardiopulmonary Resuscitation Heart Arrest
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A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions.

IEEE transactions on bio-medical engineering Oct 31, 2018
GOAL: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally demanding, and their accuracy ...
Humans Decision Support Systems, Clinical Heart Arrest Electrocardiography Support Vector Machine Wavelet Analysis Cardiopulmonary Resuscitation
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Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework.

Journal of biomedical informatics Oct 30, 2018
BACKGROUND: One of the significant problems in the field of healthcare is the low survival rate of people who have experienced sudden cardiac arrest. Early prediction of cardiac arrest can provide the time required for intervening and preventing its ...
Support Vector Machine Decision Making Medical Informatics Proportional Hazards Models Humans Heart Arrest Algorithms Databases, Factual Research Reproducibility of Results
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The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Oct 27, 2018
OBJECTIVE: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arrest (CA). Visual analysis may not extract all discriminative information. We present predictive value...
Humans Machine Learning Female Aged Male Heart Arrest Middle Aged Electroencephalography Brain Diseases Cerebral Cortex Glasgow Outcome Scale
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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...
Child, Preschool Child Intensive Care Units, Pediatric Monitoring, Physiologic Blood Pressure Oxygen Critical Illness Infant, Newborn Reproducibility of Results Sensitivity and Specificity Infant Heart Arrest Signal Processing, Computer-Assisted Heart Rate Critical Care Systole Respiratory Rate Humans Machine Learning Software
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An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

Journal of the American Heart Association Jun 26, 2018
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high fal...
Time Factors Inpatients Predictive Value of Tests Early Diagnosis Vital Signs Heart Arrest Resuscitation Seoul Adult Male Middle Aged Humans Risk Assessment Diagnosis, Computer-Assisted Decision Support Techniques Deep Learning Prognosis Retrospective Studies Female Reproducibility of Results Risk Factors Aged
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Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy: A computational study.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Jul 8, 2017
OBJECTIVE: In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities.
Netherlands Neural Networks, Computer Synapses Humans Long-Term Potentiation Female Heart Arrest Coma Aged Synaptic Transmission Hypoxia, Brain Male Membrane Potentials Middle Aged Electroencephalography
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Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.

PloS one Aug 17, 2016
INTRODUCTION: Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used r...
Aged, 80 and over Young Adult Adult Hematologic Neoplasms Male Middle Aged Adolescent Cohort Studies Models, Theoretical Treatment Outcome Monitoring, Physiologic Algorithms Critical Care Early Diagnosis Humans Prognosis Vital Signs Neural Networks, Computer Heart Arrest Retrospective Studies Machine Learning Electronic Health Records Female Aged
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Effects of Shen-Fu injection on coagulation-fibrinolysis disorders in a porcine model of cardiac arrest.

The American journal of emergency medicine Dec 12, 2015
OBJECTIVE: The objective of the study is to investigate the effects of Shen-Fu injection (SFI) on coagulation-fibrinolysis disorders in a porcine model of cardiac arrest.
Resuscitation Cardiovascular Agents Swine, Miniature Ventricular Fibrillation Injections Animals Blood Coagulation Disorders Swine Epinephrine China Phytotherapy Disease Models, Animal Heart Arrest Drugs, Chinese Herbal Fibrinolysis
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Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network.

Journal of vascular surgery May 5, 2015
OBJECTIVE: Ruptured abdominal aortic aneurysm (rAAA) carries a high mortality rate, even with prompt transfer to a medical center. An artificial neural network (ANN) is a computational model that improves predictive ability through pattern recognitio...
Risk Assessment Unconsciousness Time Factors Aortic Rupture Hospital Mortality ROC Curve Logistic Models Treatment Outcome Risk Factors Humans Aged Neural Networks, Computer Aged, 80 and over Multivariate Analysis Age Factors Algorithms Male Odds Ratio Predictive Value of Tests Decision Support Techniques Middle Aged Blood Vessel Prosthesis Implantation Aortic Aneurysm, Abdominal Databases, Factual Endovascular Procedures Area Under Curve Retrospective Studies Tennessee Shock Female Heart Arrest
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