Jenna M. Reps
Refining adverse drug reactions using association rule mining for electronic healthcare data
Reps, Jenna M.; Aickelin, Uwe; Ma, Jiangang; Zhang, Yanchun
Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. when an association between two variables is identified due to them both being associated to a third variable).
In this paper we propose a proof of concept method that learns common associations and uses this knowledge to automatically refine side effect signals (i.e. exposure-outcome associations) by removing instances of the exposure-outcome associations that are caused by confounding. This leaves the signal instances that are most likely to correspond to true side effect occurrences. We then calculate a novel measure termed the confounding-adjusted risk value, a more accurate absolute risk value of a patient experiencing the outcome within 60 days of the exposure.
Tentative results suggest that the method works. For the four signals (i.e. exposure-outcome associations) investigated we are able to correctly filter the majority of exposure-outcome instances that were unlikely to correspond to true side effects. The method is likely to improve when tuning the association rule mining parameters for specific health outcomes.
This paper shows that it may be possible to filter signals at a patient level based on association rules learned from considering patients’ medical histories. However, additional work is required to develop a way to automate the tuning of the method's parameters.
Reps, J. M., Aickelin, U., Ma, J., & Zhang, Y. (2014). Refining adverse drug reactions using association rule mining for electronic healthcare data
|Conference Name||IEEE International Conference of Data Mining: Data Mining in Biomedical Informatics and Healthcare (DMBIH) Workshop 2014|
|Publication Date||Jan 1, 2014|
|Deposit Date||Feb 26, 2015|
|Publicly Available Date||Feb 26, 2015|
|Peer Reviewed||Peer Reviewed|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf|
|Additional Information||Published in: 2014 IEEE International Conference on Data Mining Workshop. IEEE, 2014. ISBN 978-1-4799-4275-6. pp. 763-770, doi: 10.1109/ICDMW.2014.53|
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
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