Leveraging graph neural networks for supporting Automatic Triage of Patients
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866911793399463936 |
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| author | Defilippo, Annamaria Veltri, Pierangelo Lio', Pietro Guzzi, Pietro Hiram |
| author_facet | Defilippo, Annamaria Veltri, Pierangelo Lio', Pietro Guzzi, Pietro Hiram |
| contents | Patient triage plays a crucial role in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions.
Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process.
Thus, it is a process that can generate errors in emergency level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors.
Recently, a growing interest has been focused on leveraging artificial intelligence (AI) to develop algorithms able to maximize information gathering and minimize errors in patient triage processing.
We define and implement an AI based module to manage patients emergency code assignments in emergency departments. It uses emergency department historical data to train the medical decision process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, are used to accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07038 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging graph neural networks for supporting Automatic Triage of Patients Defilippo, Annamaria Veltri, Pierangelo Lio', Pietro Guzzi, Pietro Hiram Machine Learning Patient triage plays a crucial role in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors. Recently, a growing interest has been focused on leveraging artificial intelligence (AI) to develop algorithms able to maximize information gathering and minimize errors in patient triage processing. We define and implement an AI based module to manage patients emergency code assignments in emergency departments. It uses emergency department historical data to train the medical decision process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, are used to accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation. |
| title | Leveraging graph neural networks for supporting Automatic Triage of Patients |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.07038 |