  Adverse drug reactions (ADRs) are big concern for public health. ADRs are one
of most common causes to withdraw some drugs from markets. Now two major
methods for detecting ADRs are spontaneous reporting system (SRS), and
prescription event monitoring (PEM). The World Health Organization (WHO)
defines a signal in pharmacovigilance as "any reported information on a
possible causal relationship between an adverse event and a drug, the
relationship being unknown or incompletely documented previously". For
spontaneous reporting systems, many machine learning methods are used to detect
ADRs, such as Bayesian confidence propagation neural network (BCPNN), decision
support methods, genetic algorithms, knowledge based approaches, etc. One
limitation is the reporting mechanism to submit ADR reports, which has serious
underreporting and is not able to accurately quantify the corresponding risk.
Another limitation is hard to detect ADRs with small number of occurrences of
each drug-event association in the database. In this paper we propose feature
selection approach to detect ADRs from The Health Improvement Network (THIN)
database. First a feature matrix, which represents the medical events for the
patients before and after taking drugs, is created by linking patients'
prescriptions and corresponding medical events together. Then significant
features are selected based on feature selection methods, comparing the feature
matrix before patients take drugs with one after patients take drugs. Finally
the significant ADRs can be detected from thousands of medical events based on
corresponding features. Experiments are carried out on the drug Atorvastatin.
Good performance is achieved.
