PV SIGNAL DETECTION USING STATISTICAL DATA MINING METHODS
*Atharva Bharat Karale and Dr. M. D. Game
.
Abstract
Pharmacovigilance programmes monitor and help safeguarding the use
of medicines which is grave to the success of public health
programmes. Identifying new possible risks and developing risk
minimization action plans to prevent or ease these risks is at the heart
of all pharmacovigilance activities throughout the product lifecycle. In
this paper we examine the use of data mining algorithms to identify
signals from adverse events reported. The capabilities include
screening, data mining and frequency tabulation for potential signals,
including signal estimation using established statistical signal detection
methods. We have standard processes, algorithms and follow current
requirements for signal detection and risk management activities. The Safety Evaluators, who
are familiar with the current labeling, known adverse events, and mechanism of actions of
their drugs, read the reports, look for particular abnormalities or issues relative to the normal
product safety profile, and check the validity of the report. If the collection of reports is
regarded important due to abnormalities or issues after this process, the drug and adverse
event relationship is investigated more thoroughly and regulatory action may be taken. In this
paper various statistical data mining algorithms and statistical analyses used to find patterns
within sets of data at the FDA. With data mining, the FDA can improve its report analysis
process by automatically selecting the most significant reports for review as well as allowing
reviewers to view the information from all the reports received in an organized manner,
instead of having to manually consider each one. The reports that may contain serious and
unexpected adverse events.
Keywords: Adverse drug reactions, pharmacovigilance, safety signals, statistical methods.
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