AIMC Topic: Emergency Medical Services
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Automated video surveillance and machine learning: Leveraging existing infrastructure for cardiac arrest detection and emergency response activation.
Resuscitation
Feb 21, 2018
Classification of hospital admissions into emergency and elective care: a machine learning approach.
Health care management science
Nov 25, 2017
Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model f...
A Novel Artificial Intelligence System for Endotracheal Intubation.
Prehospital emergency care
Mar 17, 2016
OBJECTIVE: Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/a...
Intraoperative baseline oxygen consumption as a prognostic factor in emergency open abdominal surgery.
Journal of critical care
Nov 26, 2015
BACKGROUND: A new anesthesia system, the E-CAIOVX (GE Healthcare) enables the continuous monitoring of oxygen consumption (VO2) and carbon dioxide elimination (VCO2) during the surgical operation. The aim of this study was to evaluate the prognostic ...
Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.
Computational intelligence and neuroscience
Nov 2, 2015
Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency ma...
Demand Forecast Using Data Analytics for the Preallocation of Ambulances.
IEEE journal of biomedical and health informatics
Jun 17, 2015
The objective of prehospital emergency medical services (EMSs) is to have a short response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The geographic information system (GIS) is introd...
ASSOCIATIONS BETWEEN HEART RATE VARIABILITY AND NEED FOR LIFESAVING INTERVENTION IN A LARGE HELICOPTER EMS SERVICE.
Shock (Augusta, Ga.)
Jul 1, 2025
Background : Heart rate variability (HRV) measures give insight into the autonomic regulation of cardiac function in healthy and critically ill patients. The ease and predictive potential of HRV measures may be valuable in optimizing prehospital tria...
Assessment and Integration of Large Language Models for Automated Electronic Health Record Documentation in Emergency Medical Services.
Journal of medical systems
May 17, 2025
Automating Electronic Health Records (EHR) documentation can significantly reduce the burden on care providers, particularly in emergency care settings where rapid and accurate record-keeping is crucial. A critical aspect of this automation involves ...
Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.
BMC emergency medicine
May 13, 2025
BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEM...
Prehospital triage of trauma patients: predicting major surgery using artificial intelligence as decision support.
The British journal of surgery
Mar 28, 2025
BACKGROUND: Matching the necessary resources and facilities to attend to the needs of trauma patients is traditionally performed by clinicians using criteria-directed triage protocols. In the present study, it was hypothesized that an artificial inte...
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