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早期预测医学学生在高风险考试中的表现,使用机器学习方法

Haniye Mastour¹, Toktam Dehghani², Ehsan Moradi³

¹Department of Medical Education, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

|July 31, 2023

概括

机器学习模型可以预测医学学生在执照考试中的表现. 集合模型,如随机森林,优于经典模型,为高风险评估提供了可行的替代方案.

科学领域:

医学教育医学教育
人工智能的人工智能
生物统计学生物统计学

背景情况:

管理高风险的医疗检查是政策制定者的持续挑战.
机器学习 (ML) 为预测学生表现提供了一个潜在的解决方案,特别是在COVID-19等危机期间.
现有的预测方法面临着不平衡数据和复杂特征等挑战.

研究的目的:

开发和评估一种机器学习框架,用于预测医学学生在高风险考试中的表现.
为了比较经典的ML模型与整体ML模型对这个预测任务的有效性.

主要方法:

将经典的ML模型 (逻辑回归,SVM,KNN) 与整体模型 (投票,袋装,随机森林,ADA,XGB,堆叠) 进行比较.
用1005名医学学生的数据集在五年内评估模型歧视.
使用诸如平方根平均偏差 (RMSD) 和确定系数 (R2) 等指标评估性能.

主要成果:

整体ML模型,特别是随机森林和堆叠,在预测考试状态方面表现最佳.
随机森林模型实现了最高的R2 (0.80) 和最低的RMSD (0.077).
解剖科学,生物化学,寄生虫学和昆虫学GPA与结果有很强的相关性.

结论:

整体ML模型在预测医学学生在高风险考试中的表现方面明显优于经典模型.
开发的框架是综合基础医学科学考试 (CMBSE) 和类似评估的合适替代方案.
这种方法可以帮助有效地识别有风险和高绩效的学生.

关键词:

人工智能的人工智能是人工智能.数据挖掘是一种数据挖掘.数据科学在教育中的应用.组合模型组合模型医学教育医学教育

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