KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916938704224256 |
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| author | Nadeem, Mubaris Zenkert, Johannes Bender, Lisa Weber, Christian Fathi, Madjid |
| author_facet | Nadeem, Mubaris Zenkert, Johannes Bender, Lisa Weber, Christian Fathi, Madjid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations Nadeem, Mubaris Zenkert, Johannes Bender, Lisa Weber, Christian Fathi, Madjid Artificial Intelligence Emerging Technologies 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. |
| title | KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations |
| topic | Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2508.07834 |