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Refining Adverse Drug Reactions Using Association Rule Mining for Electronic Healthcare Data

机译：使用关联规则挖掘对电子医疗数据进行药物不良反应

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Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. When an association between two variables is identified due to them both being associated to a third variable). In this paper we propose a proof of concept method that learns common associations and uses this knowledge to automatically refine side effect signals (i.e. Exposure-outcome associations) by removing instances of the exposure-outcome associations that are caused by confounding. This leaves the signal instances that are most likely to correspond to true side effect occurrences. We then calculate a novel measure termed the confounding-adjusted risk value, a more accurate absolute risk value of a patient experiencing the outcome within 60 days of the exposure. Tentative results suggest that the method works. For the four signals (i.e. Exposure-outcome associations) investigated we are able to correctly filter the majority of exposure-outcome instances that were unlikely to correspond to true side effects. The method is likely to improve when tuning the association rule mining parameters for specific health outcomes. This paper shows that it may be possible to filter signals at a patient level based on association rules learned from considering patients' medical histories. However, additional work is required to develop a way to automate the tuning of the method's parameters.
机译：处方药的副作用很常见。电子医疗数据库提供了有效识别新副作用的机会，但是目前，由于混淆（例如，当两个变量之间的关联被识别，因为它们都与第三个变量关联时），该方法受到限制。在本文中，我们提出了一种概念证明方法，该方法可学习常见关联并使用该知识通过消除由混淆导致的暴露结果关联实例来自动细化副作用信号（即暴露结果关联）。这样就留下了最有可能与真实副作用相对应的信号实例。然后，我们计算出一种新的度量，称为混杂调整风险值，即在暴露后60天内经历结果的患者的更准确的绝对风险值。初步结果表明该方法行之有效。对于所研究的四个信号（即暴露结果关联），我们能够正确过滤掉大多数不太可能与真实副作用相对应的暴露结果实例。当针对特定健康结果调整关联规则挖掘参数时，该方法可能会得到改进。本文表明，有可能根据从考虑患者的病史中学习到的关联规则，在患者级别过滤信号。但是，需要额外的工作来开发一种自动调整方法参数的方法。

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