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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.

Authors

Jenna Reps

Jonathan M. Garibaldi

Uwe Aickelin

Daniele Soria

Jack E. Gibson

Richard B. Hubbard



Abstract

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
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6627871
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

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Copyright Statement
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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