Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/27615
Title: | A Performance Predictive Model for Emergency Medicine Residents |
Authors: | Ariaeinejad, Ali |
Advisor: | Samavi, Reza |
Department: | eHealth |
Keywords: | Machine Learning, Learning Analytics, SVM, kNN, Neural Network, Medical Education, Emergency Residency Training |
Publication Date: | 2017 |
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. |
URI: | http://hdl.handle.net/11375/27615 |
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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