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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Last data update: Apr 23, 2024
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Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS.
Rayhan Erlangga Rahadian et al. Resusc Plus 2024 18100606
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Novel machine learning model to improve performance of an early warning system in hospitalized patients: a retrospective multisite cross-validation study.
Hojjat Salehinejad et al. EClinicalMedicine 2024 66102312
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Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning.
Yiwu Sun et al. BMC Anesthesiol 2023 23(1) 178
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Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm.
Priscilla Yu et al. J Clin Med 2023 12(7)
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Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model.
Fredrik Hessulf et al. EBioMedicine 2023 89104464
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Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.
Maarten Z H Kolk et al. EBioMedicine 2023 89104462
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Tree-Based Algorithms and Association Rule Mining for Predicting Patients' Neurological Outcomes After First-Aid Treatment for an Out-of-Hospital Cardiac Arrest During COVID-19 Pandemic: Application of Data Mining.
Lin Wei-Chun et al. International journal of general medicine 2022 157395-7405
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Explainable and efficient deep early warning system for cardiac arrest prediction from electronic health records.
Tang Qinhua et al. Mathematical biosciences and engineering : MBE 2022 19(10) 9825-9841
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Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score.
Monlezun Dominique J et al. Medicina (Kaunas, Lithuania) 2022 58(8)
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How technology can save lives in cardiac arrest.
Scquizzato Tommaso et al. Current opinion in critical care 2022 28(3) 250-255
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The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial.
Winslow Christopher J et al. Critical care medicine 2022
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Using AI to predict future cardiac arrest
K O'Leary, Nature Medicine, April 14, 2022
Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.
Jentzer Jacob C et al. Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions 2022
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User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review.
Wan Yik-Ki Jacob et al. BMJ open 2022 12(1) e055525
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Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review.
Alamgir Asma et al. JMIR medical informatics 2021 9(12) e30798
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Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring.
Yijing Li et al. Computer methods and programs in biomedicine 2021 214106568
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Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients.
Monlezun Dominique J et al. Cardiology research and practice 2021 20213180987
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Accuracy of Machine Learning Models to Predict In-hospital Cardiac Arrest: A Systematic Review.
Moffat Laura M et al. Clinical nurse specialist CNS 2021 36(1) 29-44
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Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm - Machine Learning-Based Unsupervised Cluster Analysis.
Okada Yohei et al. Circulation journal : official journal of the Japanese Circulation Society 2021
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Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.
Xie Feng et al. JAMA network open 2021 4(8) e2118467
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Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.
Mayampurath Anoop et al. Critical care medicine 2021
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Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks.
Chiu Wei-Ting et al. Journal of the Formosan Medical Association = Taiwan yi zhi 2021
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Predictive Monitoring: IMPact in Acute Care Cardiology Trial (PM-IMPACCT) - A Randomized Clinical Trial Protocol.
Keim-Malpass Jessica et al. JMIR research protocols 2021
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Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks.
Chung Chen-Chih et al. Journal of the neurological sciences 2021 425117445
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Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.
Blomberg Stig Nikolaj et al. JAMA network open 2021 Jan 4(1) e2032320
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Predicting cardiac arrest in the emergency department.
Mitchell Oscar J L et al. Journal of the American College of Emergency Physicians open 2020 Aug 1(4) 321-326
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Machine Learning for Clinical Outcome Prediction.
Shamout Farah et al. IEEE reviews in biomedical engineering 2020 Jul PP
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Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.
Blomberg Stig Nikolaj et al. Resuscitation 2019 138322-329
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Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care.
Johnsson Jesper et al. Critical care (London, England) 2020 Jul 24(1) 474
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Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study.
Ueno Ryo et al. PloS one 2020 15(7) e0235835
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 23, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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