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http://hdl.handle.net/11375/27615
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DC Field | Value | Language |
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dc.contributor.advisor | Samavi, Reza | - |
dc.contributor.author | Ariaeinejad, Ali | - |
dc.date.accessioned | 2022-06-13T13:43:50Z | - |
dc.date.available | 2022-06-13T13:43:50Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://hdl.handle.net/11375/27615 | - |
dc.description.abstract | Competency-based medical education (CBME) is a paradigm of assessing resident performance through well-defined tasks, objectives and milestones. A large number of data points are generated during a five-year period as a resident accomplishes the assigned tasks. However, no tool support exists to process this data for early identification of a resident-at-risk failing to achieve future milestones. In this thesis, the implementation of CBME at McMaster's Royal College Emergency Medicine residency program was studied and the development of a machine learning algorithm (MLA) to identify patterns in resident performance was reported. The adaptivity of multiple MLAs to build a tool support for monitoring residents' progress and flagging those who are in most need of assistance in the context of emergency medicine education was evaluated. | en_US |
dc.language.iso | en | en_US |
dc.subject | Machine Learning, Learning Analytics, SVM, kNN, Neural Network, Medical Education, Emergency Residency Training | en_US |
dc.title | A Performance Predictive Model for Emergency Medicine Residents | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | eHealth | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Ariaeinejad_Ali_2017July_MSC_eHealth.pdf | 3.58 MB | Adobe PDF | View/Open |
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