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利用预测分析来支持高风险学习者参加巴基斯坦紧急医疗 (CPEM) 一年级认证计划

Saima Ali¹, Syed Ghazanfar Saleem¹, Priya Arumuganathan²

¹Department of Emergency Medicine, Indus Hospital and Health Network, Pakistan.

Medical teacher

|June 18, 2025

概括

预测分析和机器学习 (PAML) 在紧急医疗教育中准确识别了有风险的学习者. 有针对性的支持改善了结果,证明了PAML.

科学领域:

卫生专业教育 (HPE) 项目
医疗教育技术技术医学教育
在医疗保健中的数据科学.

背景情况:

预测分析和机器学习 (PAML) 越来越多地用于健康专业教育 (HPE).
应用程序包括学生入学,识别有风险的学习者和资源分配.
本研究侧重于PAML,用于识别在紧急医疗教育中的有风险的学习者.

研究的目的:

探索使用PAML在紧急医疗 (CPEM) 认证计划中识别有风险的学习者.
为改善学习成果提供有针对性的教育支持.
评估PAML模型在预测学习者表现方面的准确性.

主要方法:

比较回归树和线性回归模型,使用先前CPEM队列 (2018-2022) 的数据.
对2023年CPEM学习者队列 (n=26) 进行前性应用模型,以识别有风险的学生 (n=14).
提供额外学习支持 (ELS) 和分析模型准确性和干预影响.

主要成果:

这两种模型都显示出高精度 (回归树AUC=0.89,线性回归AUC=0.88).
回归树模型显示了稍微更好的灵敏度和特异性.
在针对性干预后,在14名被确定为有风险的学习者中,有8人显示得分有所改善.

结论:

对于识别有风险的学习者和在资源较少的环境中定制支持,PAML是可行的和有用的.
PAML可以增强专家的判断力,并促进公平的教育实践.
解决模型的局限性 (偏差,过拟合,数据不平衡) 对于高风险评估至关重要.

关键词:

巴基斯坦巴基斯坦巴基斯坦.预测分析是一种预测分析.紧急医疗教育紧急医学教育卫生专业教育教育卫生专业教育机器学习是机器学习.有针对性的干预.

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