KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations
This addresses the problem of knowledge management and decision support for first responders in time-critical emergency scenarios, representing an incremental application of existing AI and knowledge graph methods to a new domain.
The paper tackles the challenge of providing timely and personalized treatment recommendations in emergency rescue operations by developing KIRETT, a knowledge-graph-based assistant that uses AI to pre-recognize situations and offer intelligent recommendations, though no concrete performance numbers are provided.
Over the years, the need for rescue operations throughout the world has increased rapidly. Demographic changes and the resulting risk of injury or health disorders form the basis for emergency calls. In such scenarios, first responders are in a rush to reach the patient in need, provide first aid, and save lives. In these situations, they must be able to provide personalized and optimized healthcare in the shortest possible time and estimate the patients condition with the help of freshly recorded vital data in an emergency situation. However, in such a timedependent situation, first responders and medical experts cannot fully grasp their knowledge and need assistance and recommendation for further medical treatments. To achieve this, on the spot calculated, evaluated, and processed knowledge must be made available to improve treatments by first responders. The Knowledge Graph presented in this article as a central knowledge representation provides first responders with an innovative knowledge management that enables intelligent treatment recommendations with an artificial intelligence-based pre-recognition of the situation.