TY - GEN
T1 - Attributes for causal inference in electronic healthcare databases
AU - Reps, Jenna
AU - Garibaldi, Jonathan M.
AU - Aickelin, Uwe
AU - Soria, Daniele
AU - Gibson, Jack E.
AU - Hubbard, Richard B.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84889030338&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2013.6627871
DO - 10.1109/CBMS.2013.6627871
M3 - Conference contribution
AN - SCOPUS:84889030338
SN - 9781479910533
T3 - Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems
SP - 548
EP - 549
BT - Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems
T2 - 26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013
Y2 - 20 June 2013 through 22 June 2013
ER -