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An Algorithm for Mining Adverse-Event Datasets for Detection of Post Safety Concern of a Drug

Signal detection from Adverse Event Reports (AERs) is important for identifying and analysing drug safety concern after a drug has been released into the market. A safety signal is defined as a possible causal relation between an adverse event and a drug. There are a number of safety signal detection algorithms available for detecting drug safety concern. They compare the ratio of observed count to expected count to find instances of disproportionate reportings of an event for a drug or combination of events for a drug. In this thesis, we present an algorithm to mine the AERs to identify drugs which show sudden and large changes in patterns of reporting of adverse events. Unlike other algorithms, the proposed algorithm creates time series for each drug and use it to identify start of a potential safety problem. A novel vectorized timeseries utilizing multiple attributes has been proposed here. First a time series with a small time period was created; then to remove local variations of the number of reports in a time period, a time-window based averaging was done. This method helped to keep a relatively long time-series, but eliminated local variations. The steps in the algorithm include partitioning the counts on attribute values, creating a vector out of the partitioned counts for each time period, use of a sliding time window, normalizing the vectors and computing vector differences to find the changes in reporting over time. Weights have been assigned to attributes to highlight changes in the more significant attributes. The algorithm was tested with Adverse Event Reporting System (AERS) datasets from Food and Drug Administation (FDA). From AERS datasets the proposed algorithm identified five drugs that may have safety concern. After searching literature and the Internet it was found that the five drugs the algorithm identified, two were recalled, one was suspended, one had to undergo label change and the other one has a lawsuit pending against it.

Identiferoai:union.ndltd.org:UMIAMI/oai:scholarlyrepository.miami.edu:oa_theses-1016
Date01 January 2010
CreatorsBiswas, Debashis
PublisherScholarly Repository
Source SetsUniversity of Miami
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceOpen Access Theses

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