AIMC Topic: Ambulances
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Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).
Scientific reports
Apr 24, 2025
The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimately saving lives. Delays in ambulance arrival can have serious consequences for patient health and s...
LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations.
Scientific reports
Feb 19, 2025
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
Feb 12, 2025
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...
Firearm Injury Risk Prediction Among Children Transported by 9-1-1 Emergency Medical Services: A Machine Learning Analysis.
Pediatric emergency care
Dec 12, 2024
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...
Using machine learning to assess the extent of busy ambulances and its impact on ambulance response times: A retrospective observational study.
PloS one
Jan 5, 2024
BACKGROUND: Ambulance response times are considered important. Busy ambulances are common, but little is known about their effect on response times.
Few-Shot Emergency Siren Detection.
Sensors (Basel, Switzerland)
Jun 8, 2022
It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This...
Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances.
Informatics for health & social care
Dec 30, 2021
We derived machine learning models utilizing features generated by natural language processing (NLP) of free-text data from an ambulance services provider to identify fall cases. The data comprised samples of electronic patient care records care reco...
Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study.
Annals of emergency medicine
May 8, 2021
STUDY OBJECTIVE: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments.
A validation of machine learning-based risk scores in the prehospital setting.
PloS one
Dec 13, 2019
BACKGROUND: The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a 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...
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