Towards automated self-tagging in emergency health cases

In emergency health cases such as mass casualty incidents, the death ratio is still high due to lack of an automatic and intelligent system which timely observes and reports patient criticality. Indeed, the existing criticality assessment approaches are manual such as the established Simple Triage and Rapid Treatment (START). Accordingly, it is difficult for care givers to provide optimal healthcare, in particular, if the number of casualties outnumbers the responders. A challenge is how to automatically tag a possibly large number of victims with various types of disorders immediately after an incident and before the arrival of the paramedics. Such an automated tagging would provide for more optimized emergency response. We propose an automatic self-tagging methodology using body sensor networks that deliver relevant vital signs, i.e., respiratory rate, heart rate and mental status. We present three approaches to recognize and grade the criticality level of patients. The proposed approaches are generic and can be easily adapted to different scenario such as patients in intensive care units, patients in surgery and elderlies being monitored in their home. Being fully automated, our methodology is able to provide real-time tagging with higher accuracy and fine-granularity than the simplistic manual current systems. We demonstrate the viability of our self-tagging approaches by statistically demonstrating their accuracy compared to that of experts manual tagging.