自动识别反质量标准和CanMEDS在使用自然语言处理的书面反评论中的角色
Sofie Van Ostaeyen1, Loic De Langhe2, Orphée De Clercq2
1Department of Educational Sciences at Ghent University, Belgium.
Perspectives on medical education
|December 25, 2023
概括
使用大型语言模型 (LLM) 自动化书面反分析可以有效地识别反质量标准和CanMEDS在医疗保健教育中的作用,节省大量时间和资源.
科学领域:
- 医学教育 医学教育
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 在医疗保健教育中,手动分析书面反是资源密集的.
- 确定反质量标准和CanMEDS角色对于有效的培训至关重要.
- 现有的反分析方法耗时,需要大量的人力.
研究的目的:
- 为了研究微调一个最先进的大型语言模型 (LLM) 进行自动反质量评估的有效性.
- 确定法学士是否能够准确地识别预定义的反质量标准 (性能,判断,阐述,改进) 和CanMEDS角色 (医学专家,沟通者,合作者,领导者,健康倡导者,学者,专业人士) 在书面评论中.
主要方法:
- 利用了来自比利时五个医疗保健教育计划的2,349条标记反意见的数据集.
- 将数据分成12452个句子,用于机器学习分析.
- 使用荷兰BERT模型 (BERTje和RobBERT) 训练了四种多类多标签分类模型,以确定反质量标准和CanMEDS角色.
主要成果:
- 对于反质量标准,分类模型实现了宏观平均F1分数为0.73 (BERTje) 和0.76 (RobBERT).
- 模型在识别CanMEDS角色方面获得了0.71 (BERTje) 和0.72 (RobBERT) 的F1分数.
- 证明了LLM在准确检测反质量和CanMEDS角色方面的能力.
结论:
- 最先进的LLM可以有效地微调,以自动化医疗保健教育中书面反评论的质量分析.
- 使用LLM进行自动化分析,与手工方法相比,可以大大节省时间和资源.
- 这种方法有可能提高医疗培训计划中反评估的效率和可扩展性.
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