KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations

2025-08-11

AI Summary

In emergency medical scenarios, time-critical decision-making under high cognitive load impedes efficient application of domain expertise. To address this, we propose a context-aware pre-identification mechanism integrating medical knowledge graphs with lightweight artificial intelligence. Our approach constructs a structured, domain-specific knowledge graph to dynamically organize emergency care protocols and employs an efficient AI model to analyze real-time vital signs for context-sensitive, personalized treatment recommendations. The system enables on-site knowledge computation, automated logical inference, and precise clinical decision support, thereby enhancing both timeliness and accuracy of life-saving interventions. Experimental evaluation demonstrates a 37% reduction in decision response latency compared to conventional rule-based systems, with a recommendation accuracy of 91.2%. This work establishes a deployable, intelligent assistance paradigm for frontline emergency responders.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Hardware-aware ML

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Provides AI-based treatment recommendations for emergency responders
• Enhances personalized healthcare using real-time vital data analysis
• Improves knowledge management for time-sensitive rescue operations

Innovation

• Knowledge-Graph-Based smart treatment assistant
• AI-based pre-recognition of emergency situations
• Real-time vital data processing for optimized healthcare