AIMC Topic: Emergency Medical Services

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LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations.

Scientific reports
Traffic congestion, particularly in rapidly expanding urban centers, significantly impacts the timely delivery of emergency medical services (EMS), where every minute can mean the difference between life and death. Traditional traffic signal control ...

Accuracy of Commercial Large Language Model (ChatGPT) to Predict the Diagnosis for Prehospital Patients Suitable for Ambulance Transport Decisions: Diagnostic Accuracy Study.

Prehospital emergency care
OBJECTIVES: While ambulance transport decisions guided by artificial intelligence (AI) could be useful, little is known of the accuracy of AI in making patient diagnoses based on the pre-hospital patient care report (PCR). The primary objective of th...

A comparative analysis of trauma-related mortality in South Korea using classification models.

International journal of medical informatics
BACKGROUND: Reducing mortality among severe trauma patients requires the establishment of an effective emergency transportation system and the rapid transfer of patients to appropriate medical facilities. Machine learning offers significant potential...

Human-centred AI for emergency cardiac care: Evaluating RAPIDx AI with PROLIFERATE_AI.

International journal of medical informatics
BACKGROUND: Chest pain diagnosis in emergency care is hindered by overlapping cardiac and non-cardiac symptoms, causing diagnostic uncertainty. Artificial Intelligence, such as RAPIDx AI, aims to enhance accuracy through clinical and biochemical data...

A Novel Artificial Intelligence-Enhanced Digital Network for Prehospital Emergency Support: Community Intervention Study.

Journal of medical Internet research
BACKGROUND: Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Techn...

Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.

Prehospital emergency care
OBJECTIVES: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Larg...

Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study.

BMC emergency medicine
BACKGROUND: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The ri...

Firearm Injury Risk Prediction Among Children Transported by 9-1-1 Emergency Medical Services: A Machine Learning Analysis.

Pediatric emergency care
OBJECTIVE: Among children transported by ambulance across the United States, we used machine learning models to develop a risk prediction tool for firearm injury using basic demographic information and home ZIP code matched to publicly available data...

AI frontiers in emergency care: the next evolution of nursing interventions.

Frontiers in public health
This scoping review explores the utilization of artificial intelligence in emergency nursing, assessing its impact, potential benefits, and the obstacles faced in its adoption. It covers the scope of AI from advanced triage protocols to continuous mo...

Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma.

Journal of medical Internet research
BACKGROUND: Prehospital trauma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines proposed by the American College of Surgeons have proven to be relatively insensitive when identifying se...