bayesianconfiden...

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Drug Safety 2007; 30 (7): 623-625
© 2007 Adis Data Information BV. All rights reserved.
Bayesian Confidence Propagation
Neural Network
Andrew Bate
WHO Collaborating Centre for International Drug Monitoring, Uppsala Monitoring Centre
(UMC), Uppsala, Sweden
Abstract
A Bayesian confidence propagation neural network (BCPNN)-based technique
has been in routine use for data mining the 3 million suspected adverse drug
reactions (ADRs) in the WHO database of suspected ADRs of as part of the
signal-detection process since 1998. Data mining is used to enhance the early
detection of previously unknown possible drug-ADR relationships, by highlight-
ing combinations that stand out quantitatively for clinical review. Now-establish-
ed signals prospectively detected from routine data mining include topiramate
associated glaucoma, and the SSRIs with neonatal withdrawal syndrome. Recent
advances in the method and its use will be discussed: (i) the recurrent neural
network approach used to analyse cyclo-oxygenase 2 inhibitor data, isolating
patterns for both rofecoxib and celecoxib; (ii) the use of data-mining methods to
improve data quality, especially the detection of duplicate reports; and (iii) the
application of BCPNN to the 2 million patient-record IMS Disease Analyzer.
“Clearly quantitative methods assist in focussing intervals are calculated and those combinations for the attention on those areas of the WHO database which the lower limit of the 95% confidence interval likely to contain previously undetected signals.”has become newly positive are then reviewed clini-
cally. A dampening effect of the IC value towards Data mining of the WHO database of suspected
zero, particularly influential with lower numbers of adverse drug reactions (ADRs) was originally start-
cases and expected accounts, is a desirable property ed at the Uppsala Monitoring Centre (UMC) in
that was realised by a Bayesian implementation of 1995. Since 1998, data-mining analyses have been
the method. Recent developments on the method are routinely performed as part of the overall signal
described below.
detection process.[1] Its principal function is the ob-
jective initial assessment of all the drug-adverse
event combinations, highlighting a subset that is 1. Pattern Recognition
then subjected to clinical review.
In order to extract additional knowledge from the The information component (IC) is the measure
WHO database, methods have been used to find of disproportionality used for that purpose in a tool
complex patterns in the data.[2]
called the Bayesian confidence propagation neural
network (BCPNN). When the drug-event combina-In the context of drug safety, complex patterns tion is reported as often as expected, based on the include syndrome-like ADRs where various inter-overall reporting of the drug and event in the data-related components (e.g. signs or symptoms) consti-base, the IC value is close to zero. Confidence tute a unique clinical entity. A syndrome may be
624Bate reported as a group of a few symptoms in one case,proposed model resembles those referred in record and then in another case as a different or partially linkage literature used for connecting datasets. The overlapping group of symptoms. It is unlikely that IC plays a critical role in the method.
all components of a syndrome are ever listed in each Matches, or ‘hits’, receive a positive weight, for individual case report.example, if the drug is listed on the two reports or if The method aims at automatic pattern recogni-the dates of onset are the same, this is a ‘hit’. tion, that is, to find possible new clusters of adverse Mismatches, or ‘misses’, receive negative weight, events that are not known to occur together, and not e.g. if the same drug is not listed on the two reports just to find previously known syndromes in new or the dates of onset are different, that gets a nega-data. It is also intended to find as many patterns as tive score. When information is missing, the weight possible within a particular dataset. It is important is zero.
for the method to be robust in the presence of noise The weight for each field is computed and the and missing values and, because of the volume of totals are added together to provide an overall pair data; it needs to be computationally efficient.score. The score accounts for both the level of
A study was done to identify patterns of suspect-agreement and the amount of information in the ed adverse events reports with celecoxib and reports.
rofecoxib in the database,[3] and the findings were in The following variables were included in the line with previous publications on the safety profile matching process: age, gender, country, date of on-of these drugs. Work is in progress at the UMC to set, drug or substance listed, the event terms and the develop the technique further: to find unknown clus-outcome.
ters of adverse events with other variables, different The hit-miss model was piloted in a dataset from drugs and using other datasets.Norway with approximately 1600 reports from 2004
including 19 known pairs of duplicates. Seventeen
2. Data Quality pairs were highlighted by the method as possible
duplicates and, of those, 12 were among the con-Duplicates, different case reports describing the firmed duplicates. The other five highlighted pairs same ADR incident, constitute an enormous prob-were not labelled as known duplicates (assumed lem in the WHO database; as for all large datasets of false positives), although at least one of these was spontaneous reports. The extent of duplication is not later confirmed as a duplicate.
well established; but their presence clearly reduces The ability to account for near match on age and signal-detection capability.date helped in the overall performance. Although Anonymised records, as present in the WHO clusters of reports submitted by the same individual database, increase the difficulty of finding duplicat-are not necessarily duplicates, they are interesting in es. Duplicates can occur by several mechanisms,themselves and detecting this type of report cluster-such as: (i) the same incident being reported by ing can be useful.
health professionals (one or several) and also by a
pharmaceutical company (the advent of patient re-
3. Data Mining of Clinical Records porting is expected to aggravate this problem); (ii)
foreign reports that escape the screening and selec-
tive importation of domestic reports from a country The IMS UK Disease Analyzer contains month-to the WHO database; (iii) follow-up reports lacking ly, updated, computerised clinical records of pa-appropriate linkage to the original case reports.tients, as maintained by general practitioners, which
A duplicate detection method based on a hit-miss have been followed, in theory, over their whole life model was developed at the UMC to identify dupli-time. The records include diagnoses, dates, test re-cated reports.[4] The body of evidence on methods sults, hospital referrals and admissions, surgical pro-for automated duplicates detection is sparse and the cedures, notes, symptoms, signs and administrative
Bayesian Confidence Propagation Neural Network625 data. This is certainly a rich data source for finding generate hypotheses and not test them. Prospective
data mining now routinely leads to the highlighting drug safety signals.
of important signals, e.g. SSRIs and neonatal con-The dataset of about 2.4 million patients was
vulsions.[6]
analysed, including 113 million prescriptions.[5] By
Quantitative methods have been shown to im-rolling back the database, it was possible to follow
prove the data quality of the dataset by identification the evolution of the IC value for the combination of
of duplicate reports.
terbinafine and angioedema (an ADR that is now
Extension of the data-mining methods to further labelled). The first hint of association occurs in 1992
analyse potential signals is underway, looking for and rises over time to a significant disproportional-
more complex dependencies in the dataset that may ity. A labelling change for this combination was
be difficult to detect through individual clinical re-approved by the US FDA in January 2004. The pilot
view and in more complex types of data, such as investigation suggested that routine analysis of IMS
clinical records.
data could have highlighted the combination for
clinical review in 1999 or even earlier. This demon-
strates that there is potential for the method to find Acknowledgements
signals early in this type of data.
The benefits of quantitative data mining of pa-
No sources of funding were used to assist in the prepara-tient records rather than performing a formal tion of this paper. The author has no conflicts of interest that pharmacoepidemiological study are that: (i) hypo-are directly relevant to the content of this paper.
thesis generating can be done very fast; (ii) the
process is robust and can be applied to large volume
of combinations; (iii) there is no pre-defined hypo-References
thesis and therefore there is more potential to dis-
1.Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural
network method for adverse drug reaction signal generation. cover the really unexpected.
Eur J Clin Pharmacol 1998; 54 (4): 315-21
2.Orre R, Bate A, Noren GN, et al. A Bayesian recurrent neural
Weaknesses of data mining of patient records
network for unsupervised pattern recognition in large incom-rather than performing formal studies include: (i)plete data sets. Int J Neural Syst 2005; 15 (3): 207-22
when large numbers of multiple tests are executed;
3.Bate A, Noren N, Orre R, et al. Pattern detection for celecoxib
and rofecoxib in the WHO database. Pharmacoepidemiol Drug and (ii) the data capture and analysis is not optimis-
Saf 2004; 13 (S1): S323
ed for the potential association highlighted. Critical 4.Noren GN, Orre R, Bate A. A hit-miss model for duplicate review is needed for such associations, as well as
detection in the WHO drug safety database. Proceedings of the
ACM SIGKDD International Conference on Knowledge Dis-further evaluation, including formal epidemiologi-covery and Data Mining. New York: ACM Press, 2005:
cal studies.
459-68
5.Bate A, Edwards IR, Edwards J, et al. Knowledge finding in
IMS disease analyser Mediplus UK database – effective data
mining in longitudinal patient safety data. Drug Saf 2004; 27
4. Summary(12): 917-8
6.Sanz EJ, De-las-Cuevas C, Kiuru A, et al. Selective serotonin
reuptake inhibitors in pregnant women and neonatal withdraw-
al syndrome: a database analysis. Lancet 2005; 365 (9458): In conclusion, the BCPNN was developed to
482-7
enhance rather than replace traditional signal proce-
dures in the WHO database. It has been routinely
Correspondence: Dr Andrew Bate, Collaborating Centre for used as an initial filter to focus the clinical review on
International Drug Monitoring, Uppsala Monitoring Cen-those associations that are more likely to represent tre (UMC), Stora Torget 3, Uppsala, S-753 20, Sweden.
true signals. In all instances, the method aims to E-mail:***********************
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XXXX annual work summary
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