Triaging is a process in which using some basic questions and vital information of a patient is collected and a score is provided which tells the urgency to act and stabilize the patient. This is important in prioritizing patients and providing services first to those who need them the earliest. Some standard triaging systems are:

• ESI Protocol

• Canadian Triaging Index

• Australasian Severity Index

• SATS Protocol

These systems help to give a score for Traiging and tell the doctor how fast to act. But often due to resource scarcity, resource allocation of hospital services is based on who is going to be benefitted the most.

Our model uses Machine Learning Strategies for predicting the patient's outcome and prioritize them according to the severity of the outcome.

The research for developing this system has been divided into two phases.

Phase I: Developing a machine-learning model and a demo application. (Complete)

Phase II: Adoption of the system and enhancing model accuracy.