Open AccessPharmacovigilance, signal detection using statistical data mining methods
Author(s) -
G.V. Sriramakrishnan,
M. Muthu Selvam,
K. Mariappan,
G. Suseendran
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.31.13423
Subject(s) - pharmacovigilance , safeguarding , data mining , computer science , signal (programming language) , detection theory , risk analysis (engineering) , risk management , anomaly detection , data science , medicine , adverse effect , business , pharmacology , telecommunications , finance , detector , programming language , nursing
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.