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|id=Vol-1149/bd2014_wang
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https://ceur-ws.org/Vol-1149/bd2014_wang.pdf
ABSTRACTS : scientific
Causality driven data integration for
adverse drug reaction discovery
Chen Wang, Sarvnaz Karmi
CSIRO Computational Informatics
SUMMARY
We describe an ongoing effort in CSIRO for partially automating causality discovery in the Adverse Drug
Reaction (ADR) detection process. The proposed method integrates data from multiple sources based on rules
that indicate causality.
INTRODUCTION
Drug adverse reactions are a major threat to public health and impose huge costs to healthcare systems.
Dr Chen Wang Postmarketing surveillance aims to reduce the effects of adverse drug events. There are two types of ADR discovery
systems operating around the globe: passive discovery and active discovery. They differ in the data used for
Senior Research Scientist detecting unexpected harm caused by the normal use of a drug at the normal dosage as per label or prescription.
CSIRO Computational Informatics Passive ADR discovery, which has been established for decades, uses reports that are voluntarily submitted by
pharmaceutical companies, healthcare professionals and consumers. Regulatory bodies, such as TGA (Therapeutic
Goods Administration) and FDA (Food and Drug Administration), maintain very large databases of such reports
which they use to mine the potential safety signals. More recently, active discovery has been introduced. Active
chen.wang@csiro.au discovery monitors healthcare data from a variety of sources such as electronic health records, health insurance
claims, medical literature, or even recently medical forums to identify potential signals automatically using text and
data mining techniques. Active discovery intends to discover unexpected adverse events as early as possible and is
therefore also called near real-time drug safety surveillance. An example of such system is recently proposed in FDA
Sentinel initiative which relies on sharing deidentified patient data among a number of organisations.
Dr Chen Wang is a Senior Research Scientist at CSIRO
Computational Informatics. He received his PhD from Both types of ADR discoveries ultimately lead to establishing the causal relationship between a drug and unexpected
Nanjing University. His research interests are primarily in adverse reactions. Often ADR discovery starts with data-mining techniques for disproportionality detection of the
distributed, parallel and trustworthy systems. His current reports about a drug and an adverse reaction in comparison to other pairs of drugs and adverse reactions. These
work focus on data analytics systems for drug adverse potential ADRs are then examined in medication safety review and assessment meetings. The main task of these
reaction discovery. His recent work include accountable meetings is to establish the causality between a drug and its adverse reactions. This is largely a manual process
distributed systems and cloud computing. He is also in the current practice and often generates wide variability in assessment1,2,3. Even though the shortcomings of the
an Honorary Associate of the School of Information current process were recognised in 70s1, there is not much improvement in the practice of establishing causality
Technologies at the University of Sydney. Dr Chen Wang between a drug and an ADR so far. As ADR related data become increasingly accessible in electronic format and with
has industrial experience. He developed a high-throughput the increase in processing power and techniques of dealing with big data, it is now possible to introduce carefully
event delivery system and a medical image archive system, designed algorithms to assist the causality reasoning process and therefore automate some of the manual steps in
which are used by many hospitals and medical centres this assessment to reduce variability. We note that the current process, endorsed by WHO, is still largely based on
in USA. Naranjo’s questionnaire1 designed in 1981. To achieve this, there are two major requirements: first, it is essential to
understand and capture the reasoning process in the existing practice. A good reasoning process tends to minimise
the variability and inconsistency in assessment as shown in1. Second, integrating data from various sources is
essential for reaching correct conclusions in the reasoning process, e.g. additional data about background of the
patients in ADR reports may help to identify causes of an ADR. This is of course only possible with collaboration of
multiple health agencies to make such data accessible. Below, we propose a causality detection method to address
these requirements.
DESCRIPTION
Previous work trying to establish causality between a drug and its unexpected adverse reactions used a well designed
questionnaire to guide the assessment process1. The answers of these questions were assigned different scores
and the total score of each rater determines the certainty of the rater on whether a drug D causes a reaction R. A
consensus among raters served as an indicator of the causality of D and R.
Our proposed method contains two steps: (1) Design rules to capture the causality reasoning process using domain-
expertise and the current known knowledge of ADRs per each drug or active ingredient; and (2) Process different
data sources based on these rules to establish if a given drug D causes a specified adverse reaction R.
A starting point for rule identification is using the existing questionnaires, and also formalising the reasoning process
within the review and assessment teams inside the regulatory. For instance, consider a specific drug D and its
possible adverse reaction R and a given dataset S (e.g, electronic health records and clinical notes). The following
rules could be considered for causality discovery:
44 #bd14 | big data conference
1. Discontinued D, R still existed;
2. Discontinued D, R improved;
3. Readministered D, R reappeared;
4. Increased the dose of D, R became more severe;
5. Decreased the dose of D, R became less severe;
6. Factors F1, F2 and F3 cause R.
These rules capture common reasoning used in identifying whether D causes R. The set of rules are extensible. With these rules defined, the next step is to process data
based on these rules to discover causality between a drug and a given adverse reaction. In order to achieve this, we first build a data model that contains necessary data fields
required by these rules. For example, to support the rules above, we need information about actions taken on a drug by a consumer, or instructions of a medical professional
to the patient, such as the discontinuing its use, changing its dose etc. as well as additional information about other factors that may cause the adverse reaction. After the rule
list is completed, a table is constructed to capture the data model. See Table 1 for an example. The headers of Column 2 to Column 6 show a sample data model. Afterwards,
we process each data source using information extraction techniques and assisted by medical ontologies and drug knowledge repositories to populate the table. The last
column “D causes R” in Table 1 represents the decisions and is partially populated via existing knowledge.
Table 1. Summary of reports regarding drug D and suspected adverse reaction R
REPORT D DISCONTINUED D READMINISTRATERED DOSE CHANGE OTHER FACTORS R CHANGE D CAUSES R
1 Yes N/A No None Improved Yes
2 Yes N/A No Unknown No improvement Unknown
3 Yes N/A No F2 No improvement No
4 No No Decreased F3 Improved Yes
5 No No Decreased Unknown No improvement Unknown
Figure 1. A sample decision tree for ADR causality discovery
Based on the table, we use a decision tree to classify the data. Human annotated data are used as the training set. A sample decision tree classifier is shown in Figure 1. Note
that this tree only partially covers Table 1. The final decisions on causality (Yes, No, or Unknown) will be based on a threshold on the probabilities generated by decision. The
decision tree evolves as the number of confirmed causality pairs increases. As the data model is independent of underlying data sources, our method is capable of dealing
with multiple data sources as long as they contain at least some of information needed by the data model.
CONCLUSION
Causality discovery is essential to detect potential adverse drug reactions. However, the implementation challenges are extracting high quality causality information from a
variety of data and dealing with different level of credibility of information from different data sources.
REFERENCES
1. C. Naranjo, U. Busto, E. Sellers, P. Sandor, I. Ruiz, E. Roberts, E. Janecek, C. Domecq, and D. Greenblatt. A method for estimating the probability of adverse drug reactions. Clinical Pharmacology and Therapeutics, 30:239–245, 1981.
2. R. P. Naidu. Causality assessment: A brief insight into practices in pharmaceutical industry. Perspect Clin Res 2013;4:233-6.
3. N. Anderson, J. Borlak. Correlation versus causation? Pharmacovigilance of the analgesic flupirtine exemplifies the need for refined spontaneous ADR reporting. PLoS One. 2011;6(10):e25221
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