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改变检测罕见药物不良反应的范式:从传统的不成比例分析,到新的机器学习方法。

Changing paradigms in detecting rare adverse drug reactions: from disproportionality analysis, old and new, to machine learning.

机构信息

Center for Health Outcomes and PharmacoEconomic Research, University of Arizona, Tucson, AZ, USA.

Pharmaceutical Care Department, Ministry of National Guard - Health Affairs, Dammam, Saudi Arabia.

出版信息

Expert Opin Drug Saf. 2022 Oct;21(10):1235-1238. doi: 10.1080/14740338.2022.2131770. Epub 2022 Oct 4.

Abstract

PLAIN LANGUAGE SUMMARYYour physician, pharmacist, nurse, or even you can voluntarily report suspected adverse events associated with drugs. The FDA Adverse Reporting System (FAERS) and the WHO Vigibase are large databases that store individual reports of adverse drug reactions (ADRs). While some ADRs are very common, others are seen rarely. Detecting rare and very rare ADRs is extremely difficult but very important for the safe use of drugs. Databases such as FAERS and WHO Vigibase contain a large amount of data and are commonly used for analysis applying a statistical method called disproportionately analysis. This type of analysis determines whether there is a higher-than-expected number of adverse reactions for a particular drug. In the future, machine learning will complement this process by applying algorithms to the data, constructing and refining rules of inference, and building predictive models of ADRs. This paradigm change in testing for ADRs is expected to provide a better understanding of the factors impacting drug safety.

摘要

简体中文译文:您的医生、药剂师、护士,甚至您自己都可以自愿报告与药物相关的可疑不良事件。FDA 不良事件报告系统 (FAERS) 和世卫组织国际药物监测合作中心数据库 (Vigibase) 是存储药物不良反应报告的大型数据库。虽然有些不良反应很常见,但有些则很少见。检测罕见和极罕见的不良反应非常困难,但对于药物的安全使用却非常重要。FAERS 和世卫组织 Vigibase 等数据库包含大量数据,通常应用一种称为比例失调分析的统计方法进行分析。这种分析方法用于确定特定药物是否出现比预期更高数量的不良反应。未来,机器学习将通过应用算法来补充这一过程,构建和精炼推理规则,并建立不良反应预测模型。这种不良反应检测方法的范式转变有望更好地了解影响药物安全性的因素。

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