2013 EmpiricalBayesModeltoCombineSig

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Data mining is a crucial tool for identifying risk signals of potential adverse drug reactions (ADRs). However, mining of ADR signals is currently limited to leveraging a single data source at a time. It is widely believed that combining ADR evidence from multiple data sources will result in a more accurate risk identification system. We present a methodology based on empirical Bayes modeling to combine ADR signals mined from ~5 million adverse event reports collected by the FDA, and healthcare data corresponding to 46 million patients' the main two types of information sources currently employed for signal detection. Based on four sets of test cases (gold standard), we demonstrate that our method leads to a statistically significant and substantial improvement in signal detection accuracy, averaging 40% over the use of each source independently, and an area under the ROC curve of 0.87. We also compare the method with alternative supervised learning approaches, and argue that our approach is preferable as it does not require labeled (training) samples whose availability is currently limited. To our knowledge, this is the first effort to combine signals from these two complementary data sources, and to demonstrate the benefits of a computationally integrative strategy for drug safety surveillance.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 EmpiricalBayesModeltoCombineSig	Rave Harpaz William DuMouchel Paea LePendu Nigam H. Shah			Empirical Bayes Model to Combine Signals of Adverse Drug Reactions				10.1145/2487575.2488214		2013