韩国药品短缺:基于机器学习的预测模型和持续时间和因果因素分析
Hyun Soo Roe1, Da Eun Ko1, Seo Yun Nam1
1College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea.
这项研究开发了机器学习模型,以预测韩国药物短缺的持续时间和原因,确定短缺频率和药物替代品等关键因素,以告知政策干预.
科学领域:
- 制药科学 制药科学
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 药物短缺对韩国的医疗保健系统构成重大挑战.
- 以前预测和缓解药物短缺的努力是有限的.
研究的目的:
- 开发和验证用于预测药物短缺持续时间的机器学习模型.
- 确定韩国药品短缺的主要原因.
主要方法:
- 从韩国食品和药物安全部 (KMFDS) 获取了1054例监管报告的药物短缺病例 (2018-2024年).
- 开发了两个机器学习模型:一个用于持续时间估计,另一个用于原因分类 (七个类别).
- 使用随机森林对18个相关变量进行特征重要性分析.
主要成果:
- 模型1 (持续时间) 实现了62%的准确性,而短缺发生频率作为关键预测指标.
- 模型2 (原因分类) 尽管存在类不平衡,但表现强 (精度,回忆,F1得分>70%).
- 原因的重要预测因素包括短缺发生频率,替代药物的可用性和营销许可持有者的业务规模.
结论:
- 对于KMFDS,建议对经常出现短缺的药物进行持续监测.
- 将供给侧指标,特别是经济可行性纳入国家监测可以防止和减轻短缺.
- 确定了关键预测因素,为政策优先级和针对性干预提供证据,以解决药物短缺问题.
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