Data mining is intended to alert the observer to unusual relationships within a data set.
THE LIMITATIONS AND USE OF DATA
MINING
Data
mining is intended to alert the observer to unusual relationships within a data
set. It is essential to understand that in pharmacovigilance, what is reported
and contained within the data set does not represent the true epidemiology of
adverse reactions to medicines. There is the very well-known problem of
underreporting, but more than that, many coun-tries ask health professionals to
be selective in their reporting to cut down the ‘noise’.
In
the past, it has seemed reasonable for phar-macovigilance experts to reduce
their workload and avoid having to see multitudes of reports of more trivial or
well-known adverse reactions, but this has both health and methodological
consequences. It is often forgotten that ‘serious adverse and unexpected’
reactions can be preceded by less serious phenom-ena. The best known is the
xerophthalmia related to practolol being the harbinger of sclerosing
peritoni-tis. Also, the persistent reporting of a well known, to experts,
adverse reaction–product combination can be important since it may indicate
that practitioners in the field are concerned about it for some practical
reason. The reasons may be that they see the reaction more frequently than they
think they should, that there is something unusual about the duration or
severity, or that there are systematic errors associated with the use of the
product which lead to problems (similar confusing labelling of different
products, for example) (Biriell and Edwards, 1997).
Data
mining should allow for much easier and useful handling of large amounts of
information. Since the ‘triaging’ of information is done automatically, there
is no longer any need to specify that only serious and unexpected reactions
need be reported. Indeed, data mining in pharmacovigilance will function better
for us if there is a large amount of ‘ordinary’ adverse reac-tion information
to serve as the background. If we just record the serious and unexpected, only
the more serious and unexpected will stand out, progressively.This slow shift
of emphasis would be deleterious for public health.
Data
mining has its main future in the detection of complex patterns in the data. It
is possible that, if doctors reported all the medicinal product safety issues
that concern them, we would be able to identify some issues of use and poor use
of medicines which could be addressed (Edwards and Aronson, 2000).
One
problem with data mining is the temptation to turn it into data dredging. There
is a difference: data mining uses objectively predetermined (if flexible) logic
to examine relationships in data transparently with the aim of generating
hypotheses for further evaluation. Data dredging is based upon a series of
prejudiced queries which might imbue chance rela-tionships with plausibility,
and in which a strict logic or strategy is not followed.
Data
mining is proving to be a useful tool. Its full potential has not yet been
reached, and it may be that some of the current drug regulations and attitudes
may need to be reconsidered as its use becomes more widespread. In spite of its
potential as the primary search tool in pharmacovigilance, it is clear that its
use must be accompanied by the wise interpretation of the information. Since no
database is representa-tive of what truly happens, other observations,
monitoring and epidemiology must continue to be used in a complementary way.
Only by the interactive interpretation of findings using different
observational methodology are we likely to even approach the truth.
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