Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems

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dc.contributor.authorJasim, Abdulrahman Ahmed
dc.contributor.authorAta, Oğuz
dc.contributor.authorSalman, Omar Hussein
dc.date.accessioned2024-09-11T05:35:55Z
dc.date.available2024-09-11T05:35:55Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractBackground and objective: The Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT). Methods: The study's methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage. Results: The RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods. Conclusions: The efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.en_US
dc.identifier.citationJasim, A. A., Ata, O., Salman, O. H. (2024). Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems. International Journal of Medical Informatics, 192. 10.1016/j.ijmedinf.2024.105608en_US
dc.identifier.issn1386-5056
dc.identifier.issn1872-8243
dc.identifier.scopus2-s2.0-85202530335
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4896
dc.identifier.volume192en_US
dc.identifier.wosWOS:001403078000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorJasim, Abdulrahman Ahmed
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.relation.ispartofInternational Journal of Medical Informatics
dc.relation.isversionof10.1016/j.ijmedinf.2024.105608en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIoMTen_US
dc.subjectMultisource Dataen_US
dc.subjectPatient Triageen_US
dc.subjectReal-Time Triage Optimisation Framework (RTOF)en_US
dc.subjectTelemedicineen_US
dc.titleMultisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems
dc.typeArticle

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