Attributes for Causal Inference in Electronic Healthcare Databases
AuthorReps, J; Garibaldi, JM; Aickelin, U; Soria, D; Gibson, JE; Hubbard, RB
Source Title2013 IEEE 26TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)
University of Melbourne Author/sAickelin, Uwe
Document TypeConference Paper
CitationsReps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E. & Hubbard, R. B. (2013). Attributes for Causal Inference in Electronic Healthcare Databases. Rodrigues, PP (Ed.) Pechenizkiy, M (Ed.) Gama, J (Ed.) Correia, RC (Ed.) Liu, J (Ed.) Traina, A (Ed.) Lucas, P (Ed.) Soda, P (Ed.) 2013 IEEE 26TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), pp.548-549. IEEE. https://doi.org/10.1109/CBMS.2013.6627871.
Access StatusOpen Access
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.
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