Machine Learning for E-triage

Basit Öğe Kaydı
dc.contributor.authorBora, Şebnem
dc.contributor.authorKantarcı, Aylin
dc.contributor.authorErdoğan, Arife
dc.contributor.authorBeynek, Burak
dc.contributor.authorKheibari, Bita
dc.contributor.authorEvren, Vedat
dc.contributor.authorErdoğan, Mümin Alper
dc.date.accessioned2025-03-21T07:38:26Z
dc.date.available2025-03-21T07:38:26Z
dc.date.issued2022
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractDue to the rising number of visits to emergency departments all around the world and the importance of emergency departments in hospitals, the accurate and timely evaluation of a patient in the emergency section is of great importance. In this regard, the correct triage of the emergency department also requires a high level of priority and sensitivity. Correct and timely triage of patients is vital to effective performance in the emergency department, and if the inappropriate level of triage is chosen, errors in patients' triage will have serious consequences. It can be difficult for medical staff to assess patients' priorities at times, therefore offering an intelligent method will be pivotal for both increasing the accuracy of patients' priorities and decreasing the waiting time for emergency patients. In this study, we evaluate the machine learning algorithms in triage procedure. Our experiments show that Random Forest approach outperforms the others in e-triage.
dc.description.sponsorshipSET Teknoloji
dc.identifier.endpage90
dc.identifier.issn2602-4888
dc.identifier.issn2602-4888
dc.identifier.issue1
dc.identifier.startpage86
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2811
dc.identifier.urihttps://dergipark.org.tr/tr/pub/ijmsit/issue/69913/1119738
dc.identifier.volume6
dc.language.isoen
dc.publisherSET Teknoloji
dc.relation.ispartofInternational Journal of Multidisciplinary Studies and Innovative Technologies
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20250319
dc.subjectTriage
dc.subjectMachine Learning
dc.subjectEmergency Department
dc.subjectRandom Forest
dc.subjectSupport Vector Machine
dc.subjectDecision Trees
dc.subjectKth Nearest Neighbor
dc.titleMachine Learning for E-triage
dc.typeArticle

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