使用多种药物相似性网络来促进药物不良事件检测
Biswajit Padhi1, Ruoqi Liu1, Yuedi Yang2
1Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USA.
Heliyon
|January 3, 2025
概括
这项研究引入了一种新模型,用于增强药物安全信号,以便早期检测不良药物事件 (ADEs). 该方法使用多种药物特征来改善数据有限时的信号准确性,有助于药物监测.
科学领域:
- 药监和药物安全 药监和药物安全
- 计算毒理学计算毒理学
- 公共卫生信息学 公共卫生信息学
背景情况:
- 药物不良事件 (ADEs) 构成重大公共卫生风险,并带来巨大的成本.
- 早期发现ADEs对于减轻风险和降低医疗保健费用至关重要.
- 目前用于药物监督的信号检测算法需要大量数据集,这限制了它们在早期ADE检测中的有效性.
研究的目的:
- 开发和评估一种新的标签传播模型,以产生增强的药物安全信号.
- 通过使用多种药物特征,改善药物不良事件 (ADEs) 的早期检测.
- 解决现有信号检测算法的局限性,在数据稀缺的情况下.
主要方法:
- 基于各种药物特征构建多种药物相似性网络.
- 使用常规算法计算初始药物安全信号.
- 最初的信号在相似网络中传播,以产生增强的信号.
- 使用FDA不良事件报告系统 (FAERS) 数据和两个常见的信号检测算法进行评估.
主要成果:
- 拟议的模型产生了增强的药物安全信号,在早期ADE检测中表现优于标准算法.
- 结合临床前信息可以显著提高ADE信号检测的性能.
- 系统评估表明,在各种类型的ADE中,不同的药物相似性具有有效性.
结论:
- 标签传播模型为增强药物安全信号提供了一种有价值的方法,特别是用于早期检测ADE.
- 多种药物特征和临床前信息的整合加强了药物监测能力.
- 有一个网页界面可用于访问药物相似性得分,支持持续的药物安全监测.
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