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Michael Green

ORCID: 0000-0003-1507-1613
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About
Contact & Profiles
A5051711642
Research Areas
  • ECG Monitoring and Analysis
  • Acute Myocardial Infarction Research
  • Machine Learning in Healthcare
  • Cardiac Arrest and Resuscitation
  • Cardiac Imaging and Diagnostics
  • Explainable Artificial Intelligence (XAI)
  • Statistical Methods and Bayesian Inference
  • Health Systems, Economic Evaluations, Quality of Life
  • Heart Rate Variability and Autonomic Control
  • Non-Invasive Vital Sign Monitoring
  • Hydrological Forecasting Using AI
  • Statistical Methods in Clinical Trials

Lund University
 2006-2012

 Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room
OPENALEX - Publications 
Michael Green Jonas Björk Jakob Lundager Forberg Ulf Ekelund Lars Edenbrandt and 1 more Mattias Ohlsson

10.1016/j.artmed.2006.07.006 article EN Artificial Intelligence in Medicine 2006-09-08
 In search of the best method to predict acute coronary syndrome using only the electrocardiogram from the emergency department
OPENALEX - Publications 
Jakob Lundager Forberg Michael Green Jonas Björk Mattias Ohlsson Lars Edenbrandt and 2 more Hans Öhlin Ulf Ekelund

10.1016/j.jelectrocard.2008.07.010 article EN Journal of Electrocardiology 2008-09-20
 Exploring new possibilities for case-based explanation of artificial neural network ensembles
OPENALEX - Publications 
Michael Green Ulf Ekelund Lars Edenbrandt Jonas Björk Jakob Lundager Forberg and 1 more Mattias Ohlsson

10.1016/j.neunet.2008.09.014 article EN Neural Networks 2008-10-18
 Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome
OPENALEX - Publications 
Michael Green Mattias Ohlsson Jakob Lundager Forberg Jonas Björk Lars Edenbrandt and 1 more Ulf Ekelund

10.1016/j.jelectrocard.2006.12.011 article EN Journal of Electrocardiology 2007-02-09
 An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction
OPENALEX - Publications 
Jakob Lundager Forberg Ardavan Khoshnood Michael Green Mattias Ohlsson Jonas Björk and 3 more Stefan Jovinge Lars Edenbrandt Ulf Ekelund

Pre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces time acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI). In order detect all STEMI patients, the ECG should be transmitted cases of suspected cardiac ischemia. The aim this study was examine ability artificial neural network (ANN) safely reduce number ECGs by identifying without not needing PCI.Five hundred sixty ambulance care unit...

10.1186/1757-7241-20-8 article EN cc-by Scandinavian Journal of Trauma Resuscitation and Emergency Medicine 2012-01-01
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