[使用深度学习对药房临床实习的支持需求的早期预测]
Akinori Takagi1, Yutaka Masuda1, Tomoyuki Hamamoto1
1Laboratory of Applied Therapeutics, Center for Education & Research on Clinical Pharmacy, Showa Pharmaceutical University.
概括
深度学习准确地预测药学学生在社区药房临床实习期间的支持需求. 这种由人工智能驱动的方法分析每周的报告,以加强培训和教师支持.
科学领域:
- 药房 教育 教育 药房 教育
- 教育中的人工智能
背景情况:
- 日本的制药教育在2006年扩展到6年,以培养高素质的药剂师.
- 临床实习对于培养必要的药剂师素质至关重要,教师支持至关重要.
研究的目的:
- 开发一种方法来预测社区药房实习生所需的支持水平.
- 通过积极的教师支持,提高社区药房临床实习的质量.
主要方法:
- 分析社区药房实践学员提交的每周报告.
- 将深度学习算法应用于从第一个到第五周的每周报告的文本内容.
主要成果:
- 仅基于列出的支持需求来预测支持需求是无效的.
- 深度学习准确地预测了下一年需要的支持水平,准确率为97%,使用了五周的先前报告.
结论:
- 深度学习可以有效地预测社区药房实习中药房学生所需的支持水平.
- 这种基于每周报告的预测模型为优化教师支持和丰富实践培训经验提供了一种新的方法.
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