关于多药和常见糖尿病药物的药物相互作用的数据挖掘方法
Jyotsana Dwivedi1, Shubhi Kaushal1, Pranay Wal1
1PSIT-Pranveer Singh Institute of Technology (Pharmacy), Kanpur, India.
Current drug metabolism
|April 18, 2025
概括
数据挖掘有效地识别了糖尿病多药中药物相互作用 (DDI),提高了患者的安全性. 这种方法有助于制定个性化处方策略,以减轻与多种糖尿病药物相关的风险.
科学领域:
- 药理学 药理学是指药理学的学科.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 多药在糖尿病管理中常见,以实现最佳的葡萄糖控制.
- 增加多种药物的使用增加了药物相互作用 (DDI) 的风险.
- DDI可能会导致不良副作用并降低治疗疗效.
研究的目的:
- 评估一种数据挖掘方法,用于识别基于多药的药物相互作用.
- 专注于用于糖尿病护理的常见药物.
主要方法:
- 在多个科学数据库 (Scopus,科学网,PubMed等) 进行了全面的文献搜索. ) 的情况.
- 使用关键词,包括"糖尿病"",药物相互作用"",多药学"",数据挖掘"和"草药相互作用",以确定相关研究.
- 分析的出版物符合预先定义的纳入标准.
主要成果:
- 数据挖掘在常见的抗糖尿病药物中发现了显著的DDI.
- 突出显示的特定相互作用包括甲福明与含的对比剂 (乳酸的风险) 和NSAID与硫尿素 (低血糖的风险).
- 在老年患者和伴随疾病患者中观察到较高的DDI发病率;预测模型在检测DDI方面显示出高准确性.
结论:
- 数据挖掘是一种可行的方法,用于在糖尿病多药中检测和评估DDI.
- 研究结果可以为更加个性化和谨慎的处方提供信息,从而有可能提高患者的安全性.
- 未来的研究应该专注于改进这些方法,并将它们纳入临床实践.
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