TLDR: KIRETT is a wearable, AI-powered system that uses a Knowledge Graph to provide real-time, personalized treatment recommendations to first responders during rescue operations. It integrates patient vital data, situation detection, and a user-friendly interface to improve decision-making and patient care in emergency scenarios.
In today’s fast-paced world, the demand for efficient and effective rescue operations is growing. Factors like demographic shifts and increased health risks mean first responders are constantly challenged to provide rapid, personalized, and accurate medical care. In these critical, time-sensitive situations, medical professionals often need immediate assistance and recommendations to make the best decisions for patient treatment. This is where the KIRETT project steps in, offering an innovative solution to enhance emergency medical services.
KIRETT, which stands for Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations, introduces a sophisticated system designed to support first responders directly on the scene. At its core, KIRETT utilizes a Knowledge Graph (KG) – a powerful way to organize and represent complex information – to provide intelligent treatment recommendations. This system is further enhanced by artificial intelligence (AI) for pre-recognition of emergency situations.
The KIRETT system is built around a wearable device worn by first responders. This device acts as a central hub, accumulating vital treatment knowledge, real-time medical data from certified devices like the Zoll X-Series, and insights from its situation detection (SD) module. The SD module, powered by an artificial neural network (ANN), evaluates patient vital data to determine the most likely scenario, which then informs the Knowledge Graph’s treatment path recommendations.
A key aspect of KIRETT is its user-friendly interface. The simplified graphical user interface (GUI) on the wearable allows for easy interaction, displaying recommendations in text format. For patient safety, any recommended action requires active confirmation from the healthcare professional, adding an essential layer of human oversight. The system is even designed to support mass-casualty scenarios, where it can connect to multiple patients simultaneously, providing tailored recommendations and alerting responders if a patient’s condition worsens.
How the Knowledge Graph Works
A Knowledge Graph is essentially a structured database that stores information about real-world entities (like medical conditions, treatments, or medications) and their relationships. In healthcare, KGs are becoming invaluable for validating diagnoses and creating personalized treatment plans. The KIRETT Knowledge Graph is specifically designed based on standard operating procedures from rescue service manuals, making it highly relevant and accurate for emergency situations.
The KIRETT KG is dynamic and interconnected with other modules. When the KG needs specific patient data, such as temperature, it requests this information through a ‘Middleware’ component. This Middleware connects to medical devices and a database, retrieving the necessary vitals and feeding them back to the KG. Based on this data, the KG can make decisions or relay crucial information to the first responder via the GUI. If the KG cannot make a decision due to sensor limitations, missing information, or the need for human judgment, it prompts the user for input.
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Building and Evaluating the System
The construction of the KIRETT Knowledge Graph was a meticulous process, primarily based on a comprehensive manual for rescue operations. Information was carefully extracted and modeled into various node types (e.g., StartNode, DecisionNode, ProcedureNode, ActionNode) and relationship types (e.g., priority relationships, ‘yes/no’ for decisions, links to standard procedures). This manual modeling was continuously refined through regular feedback sessions with medical experts, ensuring the graph’s accuracy and medical correctness.
For instance, in a use case involving a patient with low blood sugar (hypoglycemia), the KIRETT system guides the first responder through the appropriate treatment path. It might prompt them to check the patient’s awakeness and ability to swallow, recommend oral glucose intake if applicable, and then request a recheck of the blood sugar level. The system can retrieve the latest blood sugar values from the database, displaying them to the first responder for quick decision-making.
The KIRETT project has undergone rigorous evaluation for its accuracy, completeness, consistency, and explainability. Future plans include performance and usability tests with medical experts and healthcare workers in simulated real-world scenarios, further validating its effectiveness. This ongoing development ensures that KIRETT remains a reliable and invaluable tool for emergency medical services.
For more detailed information, you can refer to the full research paper: KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations.
