AIMC Topic: Cardiopulmonary Resuscitation
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Outcome prediction with resting-state functional connectivity after cardiac arrest.
Scientific reports
Jul 16, 2020
Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential contribution of resting-state-functional magnetic resonance imaging (RS-fMRI) in predicting neurologica...
Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study.
Scandinavian journal of trauma, resuscitation and emergency medicine
Jun 25, 2020
INTRODUCTION: Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and wheth...
Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation.
Journal of medical systems
Dec 10, 2019
This study investigates the feasibility of estimation of blood pressure (BP) using a single earlobe photoplethysmography (Ear PPG) during cardiopulmonary resuscitation (CPR). We have designed a system that carries out Ear PPG for estimation of BP. In...
Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis.
Resuscitation
Jul 27, 2019
BACKGROUND: This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHCA patients.
Detecting Mistakes in CPR Training with Multimodal Data and Neural Networks.
Sensors (Basel, Switzerland)
Jul 13, 2019
This study investigated to what extent multimodal data can be used to detect mistakes during Cardiopulmonary Resuscitation (CPR) training. We complemented the Laerdal QCPR ResusciAnne manikin with the Multimodal Tutor for CPR, a multi-sensor system c...
A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis.
Resuscitation
Mar 15, 2019
BACKGROUND: Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, w...
Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.
Resuscitation
Jan 18, 2019
BACKGROUND: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine lear...
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.
PLoS medicine
Nov 30, 2018
BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admi...
Heart Arrest
Australia
Registries
New Zealand
Databases, Factual
Humans
Cardiopulmonary Resuscitation
Risk Assessment
Risk Factors
Retrospective Studies
Machine Learning
Hospital Mortality
Time Factors
Aged
Clinical Decision-Making
Decision Support Techniques
Treatment Outcome
Male
Female
Health Status
Middle Aged
A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions.
IEEE transactions on bio-medical engineering
Oct 31, 2018
GOAL: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally demanding, and their accuracy ...
Newborn self-inflating manual resuscitators: precision robotic testing of safety and reliability.
Archives of disease in childhood. Fetal and neonatal edition
Oct 18, 2018
AIM: A controlled bench test was undertaken to determine the performance variability among a range of neonatal self-inflating bags (SIB) compliant with current International Standards Organisation (ISO).
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