一个人工智能算法用于联合集群,以帮助在COVID-19大流行之前和期间的药物监测
Alexandre Destere1,2, Giulia Marchello2, Diane Merino1
1Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France.
British journal of clinical pharmacology
|February 9, 2024
概括
一种无监督的联合集群方法有效地从大型药物监督数据库中识别药物安全信号. 这种方法有助于管理报告的激增,并检测罕见的不良药物反应 (ADRs).
科学领域:
- 药物监督和药物安全研究.
- 数据挖掘和机器学习在医疗保健中的应用.
- 公共卫生和监管科学.
背景情况:
- 药物监控在现实环境中监测药物安全性,在开发过程中识别频繁的药物不良反应 (ADRs) 和在营销后罕见的ADRs.
- 数据挖掘和不成比例的方法有助于检测新的药物安全信号.
- 过多的报告量,通常由社交媒体放大,可以压倒药物监督过程,正如利沃西和COVID-19疫苗所见.
研究的目的:
- 评估监测药物安全性的无监督联合集群方法的性能.
- 评估该方法处理大量药物不良反应报告的能力.
- 通过提供高效的分析工具,帮助药物监督的人力资源.
主要方法:
- 应用了一个动态潜伏区块模型 (dLBM),一个时间依赖的协集群生成方法.
- 分析涵盖了2012年1月1日至2022年2月28日期间的45269份区域性ADR报告.
- 根据内部和相互关系,报告被分为时间,药物和ADR类别.
主要成果:
- 该dLBM模型成功地将报告分为10个时间,10个ADR和9个药物集合.
- 确定了与药物安全相关的三个重大社会问题,包括围绕COVID-19疫苗安全性的媒体作.
- 突出了特定的药物-ADR关系,如抗血小板剂,抗凝固剂和出血.
结论:
- 同聚类和DLBM是探索大型药物监督数据库的有希望的无监督工具.
- 这些方法有助于检测,探索和加强药物安全信号.
- 这种方法在分析药物不良反应报告的大幅增加方面是有效的.
相关概念视频
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