The ability of machine learning algorithms to predict defibrillation success during cardiac arrest: A systematic review-Reference-Cited by-同舟云学术

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The ability of machine learning algorithms to predict defibrillation success during cardiac arrest: A systematic review

Published:2023-04 Issue: Volume:185 Page:109755
ISSN:0300-9572
Container-title:Resuscitation
language:en
Short-container-title:Resuscitation

Author:

Sem Matthew^ORCID,Mastrangelo Emanuel,Lightfoot David,Aves Theresa,Lin Steve,Mohindra Rohit

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine,Emergency Nursing,Emergency Medicine

Reference32 articles.

1. Sudden Cardiac Death and Arrythmias;Srinivasan;Arrhyth Electrophysiol Rev,2018

2. Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest;Chicote;Entropy,2016

3. Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests;He;Crit Care,2015

4. Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning;Shandilya;BMC Med Inf Decis Making,2012

5. A probabilistic neural network as the predictive classifier of out-of-hospital defibrillation outcomes;Yang;Resuscitation,2005

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges;Resuscitation Plus;2023-09

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