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The effect of regularization on drug-reaction relationships
Journal article   Open access   Peer reviewed

The effect of regularization on drug-reaction relationships

M Mammadov, Lei Zhao and J Zhang
Optimization: a journal of mathematical programming and operations research, Vol.61(4), pp.405-422
Mini-EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, 24th (Izmir, Turkey, 23-Jun-2010–26-Jun-2010)
2012
DOI: https://doi.org/10.1080/02331934.2011.641547
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Abstract

optimization adverse drug reaction data classification regularization method ill-posed problem
The least-squares method is a standard approach used in data fitting that has important applications in many areas in science and engineering including many finance problems. In the case when the problem under consideration involves large-scale sparse matrices regularization methods are used to obtain more stable solutions by relaxing the data fitting. In this article, a new regularization algorithm is introduced based on the Karush-Kuhn-Tucker conditions and the Fisher-Burmeister function. The Newton method is used for solving corresponding systems of equations. The advantages of the proposed method has been demonstrated in the establishment of drug-reaction relationships based on the Australian Adverse Drug Reaction Advisory Committee database.

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