使用机器学习来识别关键学科类别,预测职务前和职务表现:为期8年的队列研究
Shiau-Shian Huang1,2, Yu-Fan Lin1, Anna YuQing Huang3
1Department of Medical Education, Clinical Innovation Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Journal of the Chinese Medical Association : JCMA
|April 22, 2024
概括
预测医学学生的职务成功是可能的,通过分析预医学和基础科学课程. 医学人文学科,化学和药理学等特定科目显示出对高职工表现的强大预测能力.
科学领域:
- 医学教育研究 医学教育研究
- 医疗保健中的机器学习
- 学术成绩预测预测
背景情况:
- 医学学生需要一个强大的知识基础,以医生发展.
- 职务人员的表现是一个关键的发展里程碑.
- 识别职工成功的预测因素可以优化医学教育.
研究的目的:
- 为了确定学术科目,预测医学学生的职场表现.
- 开发用于性能预测的机器学习模型.
主要方法:
- 一项队列研究分析了2011年至2019年期间毕业的医学学生的数据.
- 使用了机器学习技术,包括随机森林.
- 模型经过训练并使用10倍交叉验证进行验证.
主要成果:
- 13名医学预科和10名基础科学科目表现出预测能力 (AUC>0.7).
- 医学人文,社会学,化学,医生科学家培训,药理学,免疫学-微生物学和组织学是重要的预测因素.
- 随机森林模型实现了95%的准确性和88%的AUC来预测职员表现.
结论:
- 医学预科和基础科学科目可以预测职务人员的表现.
- 了解学科与绩效之间的关系可以提高学生对职工工作的准备.
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