基于机器学习的模型,用于预测中国湖北居民对药物安全的知识,态度和做法
Chao Mei1,2, San-Lan Wu1,2, Tao Zhou1,2
1Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Frontiers in public health
|June 18, 2025
概括
湖北居民表现出基本的药物安全知识,但需要改善态度和做法. 影响药物安全知识,态度和实践 (KAP) 的关键因素包括年龄,收入,教育,职业和保险状态.
科学领域:
- 制药科学 制药科学
- 公共卫生 公共卫生
- 卫生教育卫生教育教育
背景情况:
- 药物安全对于公共卫生至关重要.
- 了解药物安全的知识,态度和实践 (KAP) 对有针对性的干预至关重要.
- 湖北省居民的药物安全KAP状态需要评估.
研究的目的:
- 评估湖北省居民中药物安全KAP的现状.
- 确定影响药物安全性的决定因素 KAP.
- 为制定有效的教育策略提供数据.
主要方法:
- 对1,042名居民进行了标准化问卷调查.
- 用单变量和多变量物流回归分析数据.
- 机器学习模型 (XGB,FCNN,DNN) 用于预测KAP.
主要成果:
- 只有30.2%的人具有"优秀"的药物知识;态度 (10.3%) 和实践 (46.3%) 的得分较低.
- 年龄,收入,教育,职业和保险对KAP产生了重大影响.
- 机器学习模型对知识,态度,实践和KAP总分的预测准确性有所不同.
结论:
- 湖北居民拥有基本的药物安全知识,但表现出误解.
- 个人特征和社会经济因素是药物安全的重要决定因素.
- 需要量身定制的制药科学沟通策略,考虑居民的个人资料和确定的风险因素.
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