An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations

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dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.contributor.authorAhmed, Abdulaziz
dc.contributor.authorAl-Maamari, Mohammed
dc.contributor.authorFirouz, Mohammad
dc.contributor.authorDelen, Dursun
dc.date.accessioned2024-05-19T14:41:21Z
dc.date.available2024-05-19T14:41:21Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractPatient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. In this paper, the metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, and ASA-CaB. Grid search (GS), a traditional approach used for machine learning fine-tuning, is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The optimized model is used to develop an e-triage tool that can be used at EDs to predict ED patients' emergency severity index (ESI). The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, and 83.2%, respectively.en_US
dc.description.sponsorshipWe would like to extend our sincere appreciation to the editors and reviewers of the journal for their valuable time, expertise, and thoughtful feedback during the review process. Their insightful comments and constructive suggestions have significantly coen_US
dc.description.sponsorshipWe would like to extend our sincere appreciation to the editors and reviewers of the journal for their valuable time, expertise, and thoughtful feedback during the review process. Their insightful comments and constructive suggestions have significantly contributed to improving the quality and clarity of this paper.en_US
dc.identifier.doi10.1007/s10796-023-10431-4
dc.identifier.issn1387-3326
dc.identifier.issn1572-9419
dc.identifier.scopus2-s2.0-85172938167en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10796-023-10431-4
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5099
dc.identifier.wosWOS:001073344700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInformation Systems Frontiersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectE-Triage Automatonen_US
dc.subjectEmergency Severity Index (Esi)en_US
dc.subjectHyperparameter Optimizationen_US
dc.subjectSimulated Annealingen_US
dc.subjectMachine Learningen_US
dc.subjectHospital Emergency Departmenten_US
dc.titleAn Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operationsen_US
dc.typeArticleen_US

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