使用机器学习方法,预测印度普纳农村的非处方抗生素使用情况
Pravin Arun Sawant1, Sakshi Shantanu Hiralkar1, Yogita Purushottam Hulsurkar1
1Department of Health Sciences, School of Health Sciences, Savitribai Phule Pune University, Pune, India.
Epidemiology and health
|April 19, 2024
概括
普奈农村的非处方抗生素使用是使用机器学习来预测的. 关键因素包括直接购买药房和使用抗生素治疗眼睛问题的感知有用性,为公共卫生干预提供了信息.
科学领域:
- 公共卫生 公共卫生
- 传染病流行病学 传染病流行病学
- 医疗信息学 医疗信息学
背景情况:
- 非处方 (OTC) 抗生素的使用有助于抗菌素耐药性,这是一个重大的全球健康威胁.
- 了解非处方抗生素消费的驱动因素对于制定有针对性的干预措施至关重要.
- 农村人口可能表现出独特的抗生素获取和使用模式.
研究的目的:
- 鉴定印度浦那农村地区非处方抗生素使用的关键预测因素.
- 开发和验证用于预测OTC抗生素使用的机器学习模型.
- 为旨在减少不适当使用抗生素的公共卫生战略提供信息.
主要方法:
- 机器学习算法包括步骤后勤回归,拉索,随机森林,XGBoost和Boruta用于特征选择.
- 使用已识别的特征构建了回归和基于树的模型,以预测OTC抗生素的使用.
- 模型性能使用五倍交叉验证进行了评估,重点是接收器操作特征曲线 (AUROC) 下的面积和日志损失.
主要成果:
- 普奈农村地区的OTC抗生素使用率为35.9%.
- 博鲁塔算法识别的重要预测因素包括直接购买药房的感知是有用的,用于眼睛投诉的抗生素使用,以及家庭抗生素消费量的增加.
- 结合7个预测因素的XGBoost+Boruta模型实现了高预测准确度 (AUROC:0.934).
结论:
- XGBoost+Boruta模型在预测研究群体中OTC抗生素使用的准确性更高.
- 药店直接购买的感知效用,眼部疾病的抗生素使用,以及更高的消费水平是影响OTC抗生素使用的关键因素.
- 这些发现为设计有针对性的干预措施提供了基础,以促进农村环境中负责任的抗生素管理.
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