In hospital emergencies, sufficient medicine, and other necessary equipment are required and workforce planning is required to satisfy the number of constraints in emergency medicine. Demographics predict an increasingly aged society in the coming years. With the high occurrence of chronic diseases, including cardiovascular disease and stroke in older adults, there is an expectation of increases in emergency cases and the need for emergency care. The medical internet of things (MIOT) incorporated with the machine learning concept is a future extension of the vast internet of things domain in the field of biomedical applications, where interconnected devices such as worn, implanted, embedded, and swallowed devices can be located around the human body, forming a network. Interconnected devices in a framed network enable a myriad of services and applications for a wide range of sectors, including in medicine, safety, security, wellness, and entertainment, etc.

This Special Issue welcomes research focusing on setting up the guidelines and strategies for emergency medicine nationwide. Research articles containing results and surveys can be proposed for this Special Issue that provides recent technology to develop solutions for emergency medicine, such as state-of-art ambulance operation centers, ambulances with MIoT platforms, cost-effective technology, and relevant training for patient monitoring systems and management during medical emergencies, and smart wristbands for health monitoring and patient location in real-time.

Potential topics include but are not limited to the following:

• Predictive modeling to track patient conditioning systems
• AI-based medical emergency management systems
• AI-based biomedical monitoring
• Emerging e-Health Applications
• Patient tracking systems using machine learning
• Data mining and exploration of health data
• Medical decision support systems in healthcare infrastructure
• Medical data storage and communication for health care safety
• Remote health-care and health monitoring systems
• Wearable sensor integration for healthcare
• Intelligent algorithms and cloud technology health monitoring systems
• Big data analytics for healthcare systems
• Safety in biological system modeling
• Cognitive systems engineering approaches for medical emergency management systems