Mining unexpected temporal associations: Applications in detecting adverse drug reactions
Mining unexpected temporal associations: Applications in detecting adverse drug reactions
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DOI：
10.1109/titb.2007.900808
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发表时间：
2008-07-01
影响因子：
--
通讯作者：
O'Keefe, Christine M.
中科院分区：
文献类型：
--
作者：
Jin, Huidong (Warren);Chen, Jie;O'Keefe, Christine M.
在各种现实应用中，挖掘某些事件模式意外导致结果的意外事件非常有用，例如，同时服用两种药物有时会导致不良反应。这些意想不到的事件通常是意想不到的和罕见的，这使得现有的数据挖掘技术，主要是为了发现频繁模式而无效。本文提出了意外时态关联规则(UTAR)来描述它们。为了处理意想性，我们引入了一种新的兴趣度量--剩余杠杆，并开发了一种新的基于案例的排除技术来计算它。将其与一种面向事件的数据准备技术相结合来处理频率较低的情况，我们开发了一种新的算法MUTARC来寻找成对的UTAR。MUTARC用于从现实世界的医疗保健管理数据库中生成药物不良反应(ADR)信号。它不仅可靠地列出了六种已知的ADR，而且还列出了另一种可能导致肝炎的ADR，氟氯西林，这是我们的算法设计师和实验跑步者在实验之前不知道的。MUTARC比现有技术执行得更有效。这篇文章清楚地说明了从医疗保健管理数据库生成ADR信号的新方向的巨大潜力。
In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, Which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. The MUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.