可解释的人工智能用于预测医学学生在综合评估中的表现.
Haniye Mastour1,2, Toktam Dehghani3, Ehsan Moradi4
1Department of Medical Education, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Scientific reports
|July 3, 2025
概括
本研究介绍了一种机器学习框架,使用可解释的AI来预测医学学生在综合评估中的表现. 该模型准确地识别了有风险的学生,使得有针对性的干预和个性化的反能够改善教育成果.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 机器学习应用 机器学习应用
背景情况:
- 综合医疗评估至关重要,但对学生和机构来说是负担.
- 当前的人工智能模型缺乏用于教育决策的可解释性和可靠性.
- 医学教育需要预测分析,以支持学生的成功.
研究的目的:
- 开发和验证一个机器学习 (ML) 框架与可解释AI (XAI) 预测医学学生的表现.
- 整合学术和非学术属性,以提高预测准确度.
- 通过可解释的AI,为教育工作者和学习者提供可操作的见解.
主要方法:
- 在三所大学进行的回顾性队列研究.
- 使用一个堆叠的元模型,结合组合技术 (随机森林,自适应提升,XGBoost).
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 取得了高的差异性表现,AUC-ROC为0.97 (CMPIEs) 和0.99 (CCAs).
- 高F1得分为0.966 (CMPIE) 和0.994 (CCA) 证明了模型的有效性.
- 确定了高影响力的课程作为关键预测因素,并生成了个性化的风险概况.
结论:
- 通过XAI增强的ML框架准确地预测医学学生在高风险评估中的表现.
- SHAP分析提供了详细的见解,使教育工作者能够实施有针对性的干预措施和课程调整.
- 个性化反和早期支持可以提高医学学生的学习成果.
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