开发和验证一个机器学习工具,用于整形外科住院的应用程序查的开发和验证
Katherine J Zhu1, Preetham Bachina1, Matthew J Heron1
1Department of Plastic and Reconstructive Surgery, Johns Hopkins School of Medicine, Baltimore, Maryland.
Journal of surgical education
|February 4, 2026
概括
机器学习 (ML) 工具可以有效地选整形外科住院申请. 这种由人工智能驱动的系统有助于识别面试候选人,改善申请审查过程.
科学领域:
- 医学教育 医学教育
- 医学中的人工智能
- 选择外科住院的选择
背景情况:
- 整合性整形外科住院计划面临着越来越多的申请人数.
- 由于每个职位的申请数量越来越多,教师审查工作量正在增加.
- 人工智能 (AI) 为高效和整体的应用程序审查提供了解决方案.
研究的目的:
- 开发和验证一种机器学习 (ML) 工具,用于选居留申请.
- 使用 ML. 识别可能收到面试邀请的候选人.
主要方法:
- 一个综合性整形外科住院计划 (2022-2025) 的申请回顾性收集.
- 通过四个ML模型处理应用程序数据:XGBoost,随机森林,CatBoost和LightGBM.
- 在2022-2024年的数据上进行培训和验证,并对2025年的数据进行测试;对教师面试决定进行绩效评估.
主要成果:
- CatBoost 算法以 0.92 的 AUROC 和 0.668.8 的 AUPRC 显示了最高性能.
- 在F1得分最大化值时,灵敏度达到95%,特异性达到67%.
- 出版物总数是影响面试邀请决策的最重要因素.
结论:
- 一个经过验证的ML算法可以准确地协助选择居住面试对象.
- 这种人工智能工具可以提高居住申请审查的效率和整体性.
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