Identifying candidate risk factors for prescription drug side effects using causal contrast set mining

Reps, Jenna M.; Aickelin, Uwe

Abstract

Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner. Unfortunately, the ability of these databases to be used for causal inference is limited due to the passive way in which the data are collected resulting in various forms of bias. In this paper we investigate a method that can overcome these limitations and determine causal contrast set rules efficiently from big data. In particular, we present a new methodology for the purpose of identifying risk factors that increase a patients likelihood of experiencing the known rare side effect of renal failure after ingesting aminosalicylates. The results show that the methodology was able to identify previously researched risk factors such as being prescribed diuretics and highlighted that patients with a higher than average risk of renal failure may be even more susceptible to experiencing it as a side effect after ingesting aminosalicylates.

Citation

Reps, J. M., & Aickelin, U. Identifying candidate risk factors for prescription drug side effects using causal contrast set mining. Presented at Health Information Science (4th International Conference, HIS 2015, Melbourne, Australia, May 28-30)

• Conference Name: Health Information Science (4th International Conference, HIS 2015, Melbourne, Australia, May 28-30)
• End Date: May 30, 2015
• Publication Date: May 6, 2015
• Deposit Date: Oct 14, 2015
• Publicly Available Date: Oct 14, 2015
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/752557
• Publisher URL: http://link.springer.com/chapter/10.1007/978-3-319-19156-0_6
• Additional Information: Published as vol. 9085 of the series Lecture Notes in Computer Science (ISSN: 0302-9743). doi: 10.1007/978-3-319-19156-0_6. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19156-0_6