AI-Driven Triage: A Graph Neural Network Approach for Prehospital Emergency Triage Patients in IoMT-Based Telemedicine Systems

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dc.contributor.authorJasim, Abdulrahman Ahmed
dc.contributor.authorAta, Oguz
dc.contributor.authorSalman, Omar Hussein
dc.date.accessioned2025-02-06T18:01:19Z
dc.date.available2025-02-06T18:01:19Z
dc.date.issued2024
dc.departmentAltınbaş Üniversitesien_US
dc.descriptionContinental; et al.; Forvia; Hamilton; Magna; Schaeffleren_US
dc.description16th International Symposium on Electronics and Telecommunications, ISETC 2024 -- 7 November 2024 through 8 November 2024 -- Timisoara -- 205402en_US
dc.description.abstractImproper triage, overcrowding in the emergency department (ED), and long wait times for patients are some of the problems that arise from emergency triage due to a lack of medical and human resources. In emergency departments, proper patient triage is crucial for determining the urgency and appropriateness of treatment by evaluating the severity of each patient's condition. In most cases, a human operator will use the knowledge and data gathered from patient management to perform triage tactics. Hence, it is a method with the possibility of causing problems in high-priority interconnections. Currently, typical triage systems primarily rely on human recommendations, which are susceptible to personal convictions and error. Methods that maximize data collection and remove mistakes in patient triage processing are being developed with an increasing amount of interest in utilizing artificial intelligence (AI). We develop and deploy an AI module to control the distribution of emergency triage patients in EDs. We use data from past emergency room visits to educate the medical decision-making process. Patients are properly sorted into triage groups because of data including vital signs, symptoms, and an ECG. Test results have shown that the proposed algorithm is better than the currently used state-of-the-art triage techniques. In our opinion, the methods used will help healthcare professionals forecast the severity index, which in turn will help them direct the proper patient management protocols and allocate resources. © 2024 IEEE.en_US
dc.identifier.doi10.1109/ISETC63109.2024.10797314
dc.identifier.isbn979-835039086-5
dc.identifier.scopus2-s2.0-85216014430
dc.identifier.urihttps://doi.org/10.1109/ISETC63109.2024.10797314
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5320
dc.indekslendigikaynakScopus
dc.institutionauthorJasim, Abdulrahman Ahmed
dc.institutionauthorAta, Oğuz
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2024 16th International Symposium on Electronics and Telecommunications, ISETC 2024 - Conference Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250206
dc.subjectAI-Driven Triageen_US
dc.subjectGNNen_US
dc.subjectIoMTen_US
dc.subjectPrehospital Emergencyen_US
dc.subjectTelemedicineen_US
dc.titleAI-Driven Triage: A Graph Neural Network Approach for Prehospital Emergency Triage Patients in IoMT-Based Telemedicine Systemsen_US
dc.typeConference Objecten_US

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