Leveraging graph neural networks for supporting Automatic Triage of
  Patients
Annamaria Defilippo Pierangelo Veltri Pietro Lio' Pietro Hiram Guzzi
Patient triage plays a crucial role in emergency departments, ensuring timely
and appropriate care based on correctly evaluating the emergency grade of
patient conditions.
  Triage methods are generally performed by human operator based on her own
experience and information that are gathered from the patient management
process.
  Thus, it is a process that can generate errors in emergency level
associations. Recently, Traditional triage methods heavily rely on human
decisions, which can be subjective and prone to errors.
  Recently, a growing interest has been focused on leveraging artificial
intelligence (AI) to develop algorithms able to maximize information gathering
and minimize errors in patient triage processing.
  We define and implement an AI based module to manage patients emergency code
assignments in emergency departments. It uses emergency department historical
data to train the medical decision process. Data containing relevant patient
information, such as vital signs, symptoms, and medical history, are used to
accurately classify patients into triage categories. Experimental results
demonstrate that the proposed algorithm achieved high accuracy outperforming
traditional triage methods. By using the proposed method we claim that
healthcare professionals can predict severity index to guide patient management
processing and resource allocation.