Attributes for causal inference in electronic healthcare databases

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

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.

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.

• 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: https://nottingham-repository.worktribe.com/output/1005448
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6627871
• Additional Information: Published in: 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), © IEEE, 2013, doi: 10.1109/CBMS.2013.6627871