Attributes for causal inference in electronic healthcare databases
Reps, Jenna; Garibaldi, Jonathan M.; Aickelin, Uwe; Soria, Daniele; Gibson, Jack E.; Hubbard, Richard B.
Jonathan M. Garibaldi
Jack E. Gibson
Richard B. Hubbard
Side effects of prescription drugs present a serious issue.
Existing algorithms that detect side effects generally
require further analysis to confirm causality. In this paper
we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria.
|Publication Date||Jan 1, 2013|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2013). Attributes for causal inference in electronic healthcare databases|
|Keywords||Biomedical Informatics, Data Mining|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf|
|Additional Information||Published in: 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), © IEEE, 2013, doi: 10.1109/CBMS.2013.6627871|
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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