Smart Health Software to Support Rescue Personnel in Emergency Situations
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917743126642688 |
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| author | Ahammed, Abu Shad Obermaisser, Roman |
| author_facet | Ahammed, Abu Shad Obermaisser, Roman |
| contents | Rescue stations around the world receive millions of emergency rescue calls each year, most of which are due to health complications. Due to the high frequency and necessity of rescue services, there is always an increasing demand for quick, accurate, and coordinated responses from rescue personnel to save lives and mitigate damage. This paper introduces a rescue health management software solution designed to improve the efficiency and effectiveness of rescue situational awareness by rapidly assessing the health status of emergency patients using AI-driven decision support systems. The novelty in this software approach is it's user-centered design principles to ensure that its solutions are specifically tailored to meet the unique requirements of emergency responders. It used pre-trained machine learning models with rescue data and accepted new patient's input data to provide a probability of the major health complications so that rescue personnel can expedite treatment plan following the outcome. The paper focuses primarily on the software development and implementation steps with three use cases, while also providing a short overview of the previous machine learning-based development phases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03739 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Smart Health Software to Support Rescue Personnel in Emergency Situations Ahammed, Abu Shad Obermaisser, Roman Logic Rescue stations around the world receive millions of emergency rescue calls each year, most of which are due to health complications. Due to the high frequency and necessity of rescue services, there is always an increasing demand for quick, accurate, and coordinated responses from rescue personnel to save lives and mitigate damage. This paper introduces a rescue health management software solution designed to improve the efficiency and effectiveness of rescue situational awareness by rapidly assessing the health status of emergency patients using AI-driven decision support systems. The novelty in this software approach is it's user-centered design principles to ensure that its solutions are specifically tailored to meet the unique requirements of emergency responders. It used pre-trained machine learning models with rescue data and accepted new patient's input data to provide a probability of the major health complications so that rescue personnel can expedite treatment plan following the outcome. The paper focuses primarily on the software development and implementation steps with three use cases, while also providing a short overview of the previous machine learning-based development phases. |
| title | Smart Health Software to Support Rescue Personnel in Emergency Situations |
| topic | Logic |
| url | https://arxiv.org/abs/2408.03739 |