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An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest

Journal of the American Heart Association, 2018-07, Vol.7 (13)

2018

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Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

Journal of the American Heart Association, 2020-04, Vol.9 (7), p.e014717-e014717

2020

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Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Scientific reports, 2020-11, Vol.10 (1), p.20495-20495, Article 20495

2020

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Deep learning for predicting in‐hospital mortality among heart disease patients based on echocardiography

Echocardiography (Mount Kisco, N.Y.), 2019-02, Vol.36 (2), p.213-218

2019

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Validation of deep-learning-based triage and acuity score using a large national dataset

PloS one, 2018-10, Vol.13 (10), p.e0205836

2018

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Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography

The Journal of heart and lung transplantation, 2020-08, Vol.39 (8), p.805-814

2020

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Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography

Europace (London, England), 2020-03, Vol.22 (3), p.412-419

2020

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Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

PloS one, 2019-10, Vol.14 (10), p.e0224502-e0224502

2019

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Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure, electroencephalogram, and electrocardiogram: Retrospective study

PloS one, 2022-08, Vol.17 (8), p.e0272055-e0272055

2022

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Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

PloS one, 2019-07, Vol.14 (7), p.e0219302-e0219302

2019

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Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes

Resuscitation, 2019-06, Vol.139, p.84-91

2019

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Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Scandinavian journal of trauma, resuscitation and emergency medicine, 2020-03, Vol.28 (1), p.17-17, Article 17

2020

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Artificial intelligence for detecting mitral regurgitation using electrocardiography

Journal of electrocardiology, 2020-03, Vol.59, p.151-157

2020

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Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

Korean Circulation Journal, 2019, 49(7), , pp.629-639

2019

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Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Annals of noninvasive electrocardiology, 2021-05, Vol.26 (3), p.e12839-n/a

2021

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Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

Journal of medical Internet research, 2022-07, Vol.24 (7), p.e37928-e37928

2022

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Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables

Internal Medicine, 2024/03/15, Vol.63(6), pp.773-780

2024

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Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Scandinavian journal of trauma, resuscitation and emergency medicine, 2020-10, Vol.28 (1), p.98-98, Article 98

2020

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ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department

Clinical and Experimental Emergency Medicine, 2023, 10(4), , pp.438-445

2023

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Quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score for Predicting Clinical Deterioration in General Ward Patients Regardless of Suspected Infection

Journal of Korean Medical Science, 2022, 37(16), , pp.1-11

2022

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Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors

Korean Circulation Journal, 2023, 53(11), , pp.758-771

2023

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Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG

Diagnostics (Basel), 2022-03, Vol.12 (3), p.654

2022

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Deep learning model integrating radiologic and clinical data to predict mortality after ischemic stroke

Heliyon, 2024-05, Vol.10 (10), p.e31000-e31000, Article e31000

2024

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Deep-learning model for screening sepsis using electrocardiography

Scandinavian journal of trauma, resuscitation and emergency medicine, 2021-10, Vol.29 (1), p.145-145, Article 145

2021

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Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning

Acute and Critical Care, 2018, 33(3), , pp.117-120

2018

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An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Deep learning for predicting in‐hospital mortality among heart disease patients based on echocardiography

Validation of deep-learning-based triage and acuity score using a large national dataset

Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography

Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure, electroencephalogram, and electrocardiogram: Retrospective study

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Artificial intelligence for detecting mitral regurgitation using electrocardiography

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables

Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score for Predicting Clinical Deterioration in General Ward Patients Regardless of Suspected Infection

Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors

Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG

Deep learning model integrating radiologic and clinical data to predict mortality after ischemic stroke

Deep-learning model for screening sepsis using electrocardiography

Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning

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