Artificial intelligence in emergency medicine: a scoping review

A Kirubarajan, A Taher, S Khan… - Journal of the American …, 2020 - Wiley Online Library
Introduction Despite the growing investment in and adoption of artificial intelligence (AI) in
medicine, the applications of AI in an emergency setting remain unclear. This scoping …

Artificial intelligence and machine learning applications in sudden cardiac arrest prediction and management: a comprehensive review

S Aqel, S Syaj, A Al-Bzour, F Abuzanouneh… - Current Cardiology …, 2023 - Springer
Abstract Purpose of Review This literature review aims to provide a comprehensive
overview of the recent advances in prediction models and the deployment of AI and ML in …

Generating adversarial samples on multivariate time series using variational autoencoders

S Harford, F Karim, H Darabi - IEEE/CAA Journal of Automatica …, 2021 - ieeexplore.ieee.org
Classification models for multivariate time series have drawn the interest of many
researchers to the field with the objective of developing accurate and efficient models …

Machine Learning Models for Survival and Neurological Outcome Prediction of Out‐of‐Hospital Cardiac Arrest Patients

CY Cheng, IM Chiu, WH Zeng… - BioMed Research …, 2021 - Wiley Online Library
Background. Out‐of‐hospital cardiac arrest (OHCA) is a major health problem worldwide,
and neurologic injury remains the leading cause of morbidity and mortality among survivors …

Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan

Y Kawai, K Yamamoto, K Miyazaki, H Asai… - Scientific Reports, 2023 - nature.com
Refining out-of-hospital cardiopulmonary arrest (OHCA) resuscitation protocols for local
emergency practices is vital. The lack of comprehensive evaluation methods for …

[HTML][HTML] Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

J Toy, N Bosson, S Schlesinger, M Gausche-Hill… - Resuscitation …, 2023 - Elsevier
Background Artificial intelligence (AI) has demonstrated significant potential in supporting
emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; …

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 …

A machine learning approach for modeling decisions in the out of hospital cardiac arrest care workflow

S Harford, M Del Rios, S Heinert, J Weber… - BMC medical informatics …, 2022 - Springer
Background A growing body of research has shown that machine learning (ML) can be a
useful tool to predict how different variable combinations affect out-of-hospital cardiac arrest …

Visual assessment of interactions among resuscitation activity factors in out-of-hospital cardiopulmonary arrest using a machine learning model

Y Kawai, H Okuda, A Kinoshita, K Yamamoto… - Plos one, 2022 - journals.plos.org
Aim The evaluation of the effects of resuscitation activity factors on the outcome of out-of-
hospital cardiopulmonary arrest (OHCA) requires consideration of the interactions among …

Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis

A Zobeiri, A Rezaee, F Hajati, A Argha… - International Journal of …, 2024 - Elsevier
Background Early and reliable prognostication in post-cardiac arrest patients remains
challenging, with various factors linked to return of spontaneous circulation (ROSC) …