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Attributes for causal inference in electronic healthcare databases

Reps, Jenna; Garibaldi, Jonathan M.; Aickelin, Uwe; Soria, Daniele; Gibson, Jack E.; Hubbard, Richard B.


Jenna Reps

Jonathan M. Garibaldi

Uwe Aickelin

Daniele Soria

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.


Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2013). Attributes for causal inference in electronic healthcare databases.

Conference Name CBMS 2013, The 26th IEEE International Symposium on Computer-Based Medical Systems, Porto
End Date Jun 22, 2013
Publication Date Jan 1, 2013
Deposit Date Sep 29, 2014
Publicly Available Date Sep 29, 2014
Peer Reviewed Peer Reviewed
Keywords Biomedical Informatics, Data Mining
Public URL
Publisher URL
Additional Information Published in: 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), © IEEE, 2013, doi: 10.1109/CBMS.2013.6627871


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