Description of Data Mining Methodology Used by the Uppsala Monitoring Centre

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Chapter: Pharmacovigilance: Data Mining in Pharmacovigilance

The UMC is, as the WHO Collaborating Centre for International Drug Monitoring, responsible for the technical and scientific maintenance and development of the WHO International Drug Monitoring Programme.


DESCRIPTION OF DATA MINING METHODOLOGY USED BY THE UPPSALA MONITORING CENTRE

The UMC is, as the WHO Collaborating Centre for International Drug Monitoring, responsible for the technical and scientific maintenance and development of the WHO International Drug Monitoring Programme. The Programme now has 76 member countries, annually contributing around 250 000 suspected adverse drug reaction (ADR) reports to the WHO database in Uppsala.

One of the main aims of the international phar-macovigilance programme is to identify early signals of safety problems related to medicines. To aid this, a new ADR signalling system has been provided for national monitoring centres and authorities based on automated exploratory data analysis. It comple-ments the previous signal detection procedure which involved the examination of unwieldy, large amounts of sorted and tabulated material by an expert panel. An overview of the new signalling approach, including results from the first part of an evaluation including a comparison against another signalling system has been published (Lindquist et al., 1999).

The UMC’s main purpose is to find novel drug safety signals: new information. From experience a principal argument has evolved in drug safety, that, if important signals shall not be missed, the first analy-sis of information should be free from prejudice and a priori thinking. Quantitative filtering of the data focuses clinical review on the most potentially impor-tant drug adverse reaction combinations (Bate et al., 1998; Lindquist et al., 1999, 2000; Orre et al., 2000). Human intelligence and experience is able to oper-ate better with a transparent filtering method in the generation of hypotheses.

The BCPNN is a computational framework based on a statistical neural network where learning and inference is done using the principles of Bayes law. The network can take real, discrete and binary vari-ables as input. It is in its most simple feed forward implementation equivalent to the naïve Bayes clas-sifier, which is a standard prediction algorithm that has proven efficient in many applications (Domingos and Pazzani, 1997; Hand and Yu, 2001). The BCPNN can also be implemented with feedback loops between the input and the output layers as a form of Hopfield network for unsupervised pattern recognition (Lansner and Ekeberg, 1989). As such, it has been demon-strated useful in identifying ADR syndromes based on spontaneous reporting data (Orre et al., 2005). The BCPNN has also been extended to a multilayer network (Holst, 1997), which has been successfully applied to areas like diagnosis (Holst and Lansner, 1996), expert systems (Holst and Lansner, 1993) and data analysis in pulp and paper manufacturing (Orre and Lansner, 1996). Related research has produced methods to handle uncertainty in Bayes classification (Norén and Orre, 2005).

The BCPNN is transparent, in that it is easy to see what has been calculated, and the results are reproducible, making validation and checking simple. The network is easy to train; it only takes one pass across the data, which makes it highly time effi-cient. Because only a small proportion of all possible drug-adverse reaction combinations are actually non-zero in the database, the use of sparse matrix meth-ods makes searches through the database quick and efficient.

The weights in the BCPNN are referred to as Infor-mation Components (IC). They are the basis for the data mining method used to screen the WHO database for unexpectedly strong dependencies between vari-ables (e.g. drugs and adverse reactions). They can also be used to study how dependencies in the database change on addition of new data. The IC between drug x and ADR y is defined as (Bate et al., 1998; Orre et al., 2000):


IC = log2 pxy/pxpy

where

px = probability of drug x being listed on a case report

py = probability of ADR y being listed on a case report

pxy = probability that drug x and ADR y are listed on the same case report.

In principle, the IC value is based on:

• the number of case reports with drug x (cx);

• the number of case reports with ADR y (cy);

• the number of reports with the specific combination (cxy); and

• the total number of reports C.

Positive IC values indicate that the particular combi-nation is reported to the database more often than what can be expected based on the general reporting of the two terms in the database. The higher the IC value, the more the combination stands out from the back-ground. An adjusted IC estimate can also be calculated to control for possible confounding variables (Norén, Bate and Orre, 2004). Stratified analyses are part of the routine data mining of the WHO database but the first pass analysis is unadjusted since it remains unclear how to best use adjustment in routine data mining (Bate et al., 2003).

From the IC probability distribution, expectation and variance values are calculated using Bayesian statistics. Thus estimates of precision (standard devi-ations) are provided for each point estimate of the IC, allowing both the point estimate and the asso-ciated uncertainty to be examined. The interpreta-tion of the probability distribution is intuitive: the standard deviation for each IC provides a measure of the robustness of the estimate. The higher the cx, cy and cxy levels are, the narrower the confi-dence interval becomes. If a positive IC value increases over time and the confidence interval narrows, this indicates an increased certainty of a positive quantitative association between the studied variables.

The BCPNN framework provides an efficient computational model for the analysis of large amounts of data and combinations of variables, whether real, discrete or binary. The efficiency is enhanced by the IC being the weight between nodes in the neural network. The BCPNN can be used both for data anal-ysis/data mining, prediction and unsupervised pattern recognition. Bayesian statistics fits intuitively into the framework of a neural network approach as both build on the concept of adapting on the basis of new data. The method has also been extended to detect dependencies between several variables (Orre et al., 2000). Pattern recognition by the BCPNN does not depend upon any a priori hypothesis, as an unsu-pervised learning approach is used. This is useful in new syndrome detection, finding patient age profiles of drug-adverse reactions, determining at-risk groups and dose relationships; and can thus be used to find complex dependencies which have not necessarily been considered before. Naturally, changes in patterns may also be important.

The automated routine data mining of the WHO database is based on using the BCPNN to scan incom-ing ADR reports and compare them statistically with what is already stored in the database. A new quarterly output to national pharmacovigilance centres contains statistical information from the BCPNN scan. It also contains frequency counts for each drug and ADR listed, both individually and occurring together. The figures from the previous quarter are also included and the data is provided in a computerised format. Drug-adverse reaction combinations with IC values above a certain threshold are selected for further consider-ation (‘associations’). As an important complement, a triage algorithm has been designed to focus atten-tion on the most urgent issues from an international perspective (Ståhl et al., 2004) (see Table 21.1, for the fundamental triage algorithm criteria). The case series thus highlighted by the UMC are forwarded to a panel of clinical reviewers for evaluation and expert opinion. As previously, signals of possible safety problems are circulated to all national centres participating in the international pharmacovigilance programme for consideration of public health impli-cations. Drug safety issues first highlighted with the UMC’s data mining system have also been published in the mainstream medical literature (Coulter et al., 2001; Sanz et al., 2005).


Automated duplicate detection is another important area of application for data mining methods in phar-macovigilance. The analysis of spontaneous reporting data is sometimes impaired by poor data quality, and the presence of duplicate case reports is an especially important data quality problem. Sometimes different sources provide separate case reports for the same ADR incident and other times there are mistakes in linking to existing database records any follow-up case reports submitted to update the original report. With the ultimate aim of improving data analysis in the WHO database, the UMC has developed a statistical method for automated duplicate detection in spontaneous reporting data (Norén, Orre and Bate, 2005). The primary aim is data cleaning, which is an important component of the knowledge discovery process (Fayyad, Piatetsky-Shapiro and Smyth, 1996), but duplicate detection can also be considered as a form of data mining in its own right.

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