Developing a comparative framework for machine-learning classifying models based on the Emergency Severity Index triage system

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dc.contributor.authorKarajeh, Ala'
dc.contributor.examiningcommitteeSherif, Sherif (Electrical and Computer Engineering)
dc.contributor.examiningcommitteeHenry, Christopher (Computer Science)
dc.contributor.supervisorEskicioglu, Rasit
dc.date.accessioned2024-01-03T22:10:08Z
dc.date.available2024-01-03T22:10:08Z
dc.date.issued2024-01-02
dc.date.submitted2024-01-02T22:05:35Zen_US
dc.degree.disciplineBiomedical Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)
dc.description.abstractEmergency departments are among the most crowded facilities in healthcare premises, where they receive a variety of cases, including critical ones and life-threatening conditions. Arranging the order of received patients and providing timely and efficient care is of utmost importance. This procedure is usually carried out by a nurse who considers the patient’s symptoms and vital signs besides ready resources. Existing literature revealed that there is variability in the accuracy of the triage process inside emergencies for a variety of reasons. Therefore, developing an aid tool based on Machine Learning (ML) algorithms would help mitigate this issue and improve the workflow inside such a crucial setting. This work provides a comparison between several ML-based classifying models that were developed from MIMIC-IV-ED and MIMIC-IV databases. Moreover, it presents insights into hidden patterns that explain some outcomes of subgroups in the examined individuals.
dc.description.noteFebruary 2024
dc.identifier.urihttp://hdl.handle.net/1993/37906
dc.language.isoeng
dc.rightsopen accessen_US
dc.subjectEmergency triage enhancement
dc.subjectMachine learning-based triage
dc.subjectEmergency care analytics
dc.subjectMachine learning-based ESI
dc.subjectHealthcare analytics
dc.titleDeveloping a comparative framework for machine-learning classifying models based on the Emergency Severity Index triage system
dc.typemaster thesisen_US
local.subject.manitobano
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