Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).

Area of Science:

• Emergency medicine
• Computer science
• Artificial intelligence

Background:

• Ambulance dispatch systems are vital for emergency medical care, but delays can negatively impact patient survival.
• Efficient communication and timely ambulance arrival are critical for effective emergency response.

Purpose of the Study:

• To develop and evaluate a machine learning-based ambulance system to improve emergency preparedness and response times.
• To optimize ambulance distribution and real-time routing for critical patient care.

Main Methods:

• Decision trees were employed for analyzing historical data to predict ambulance demand.
• Support Vector Machine (SVM) was utilized to optimize the distribution of limited ambulance resources based on patient data.
• A Convolutional Neural Network (CNN)-based deep learning model was implemented for real-time route optimization considering traffic conditions.

Main Results:

• The CNN-based deep learning model achieved 99.15% accuracy in real-time route optimization.
• The system enhances dispatch efficiency and communication, particularly during peak demand periods.
• Predictive modeling aids in proactive ambulance resource planning.

Conclusions:

• The proposed machine learning-based ambulance system significantly improves dispatch efficiency and reduces response times.
• This system enhances the overall effectiveness of emergency medical services, leading to better patient outcomes.
• The integration of AI in ambulance dispatching is crucial for modern emergency preparedness.