Mining Candidates for Adverse Drug Interactions in Electronic Patient Records
2014 (English)In: PETRA '14 Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, PETRA’14, New York: ACM Press, 2014Conference paper (Refereed)
Electronic patient records provide a valuable source of information for detecting adverse drug events. In this paper, we explore two different but complementary approaches to extracting useful information from electronic patient records with the goal of identifying candidate drugs, or combinations of drugs, to be further investigated for suspected adverse drug events. We propose a novel filter-and-refine approach that combines sequential pattern mining and disproportionality analysis. The proposed method is expected to identify groups of possibly interacting drugs suspected for causing certain adverse drug events. We perform an empirical investigation of the proposed method using a subset of the Stockholm electronic patient record corpus. The data used in this study consists of all diagnoses and medications for a group of patients diagnoses with at least one heart related diagnosis during the period 2008--2010. The study shows that the method indeed is able to detect combinations of drugs that occur more frequently for patients with cardiovascular diseases than for patients in a control group, providing opportunities for finding candidate drugs that cause adverse drug effects through interaction.
Place, publisher, year, edition, pages
New York: ACM Press, 2014.
Sequence mining, sequential patterns, disproportionality analysis, adverse drug effects, health records
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-111020DOI: 10.1145/2674396.2674420ISBN: 978-1-4503-2746-6OAI: oai:DiVA.org:su-111020DiVA: diva2:773806
7th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 14