Random forest-based prediction of stroke outcome
We research into the clinical, biochemical and neuroimaging factors associated with the
outcome of stroke patients to generate a predictive model using machine learning …
outcome of stroke patients to generate a predictive model using machine learning …
Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook
Abstract The Internet of Health Things promotes personalized and higher standards of care.
Its application is diverse and attracts the attention of a substantial section of the scientific …
Its application is diverse and attracts the attention of a substantial section of the scientific …
Artificial intelligence and machine learning in prehospital emergency care: A scoping review
ML Chee, ML Chee, H Huang, K Mazzochi, K Taylor… - Iscience, 2023 - cell.com
Our scoping review provides a comprehensive analysis of the landscape of artificial
intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field …
intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field …
Deep neural networks for ECG-based pulse detection during out-of-hospital cardiac arrest
The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary
for the early recognition of the arrest and the detection of return of spontaneous circulation …
for the early recognition of the arrest and the detection of return of spontaneous circulation …
Novel density poincaré plot based machine learning method to detect atrial fibrillation from premature atrial/ventricular contractions
Objective: Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and
premature ventricular contraction (PVC) is difficult as frequent occurrences of these ectopic …
premature ventricular contraction (PVC) is difficult as frequent occurrences of these ectopic …
Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms
We present the results from a white-box machine learning approach to detect cardiac
arrhythmias using electrocardiographic data. A C5. 0 is trained to recognize four classes …
arrhythmias using electrocardiographic data. A C5. 0 is trained to recognize four classes …
Atrial fibrillation prediction from critically ill sepsis patients
Sepsis is defined by life-threatening organ dysfunction during infection and is the leading
cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial …
cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial …
Multimodal algorithms for the classification of circulation states during out-of-hospital cardiac arrest
Goal: Identifying the circulation state during out-of-hospital cardiac arrest (OHCA) is
essential to determine what life-saving therapies to apply. Currently algorithms discriminate …
essential to determine what life-saving therapies to apply. Currently algorithms discriminate …
Capnography: A support tool for the detection of return of spontaneous circulation in out-of-hospital cardiac arrest
Background Automated detection of return of spontaneous circulation (ROSC) is still an
unsolved problem during cardiac arrest. Current guidelines recommend the use of …
unsolved problem during cardiac arrest. Current guidelines recommend the use of …
Automatic cardiac rhythm classification with concurrent manual chest compressions
Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA)
rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy …
rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy …