Examining the effect of prescription sequence on developing adverse drug reactions: The case of renal failure in diabetic patients

Behrooz Davazdahemami, Dursun Delen

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Objectives: While the effect of medications in development of Adverse Drug Reactions (ADRs) have been widely studied in the past, the literature lacks sufficient coverage in investigating whether the sequence in which [ADR-prone] drugs are prescribed (and administered) can increase the chances of ADR development. The present study investigates this potential effect by applying emergent sequential pattern mining techniques to electronic health records. Materials and methods: Using longitudinal medication and diagnosis records from more than 377,000 diabetic patients, in this study, we assessed the possible effect of prescription sequences in developing acute renal failure as a prevalent ADR among this group of patients. Relying on emergent sequential pattern mining, two statistical case-control approaches were designed and employed for this purpose. Results: The results taken from the two employed approaches (i.e. 76.7% total agreement and 68.4% agreement on the existence of some significant effect) provide evidence for the potential effect of prescription sequence on ADRs development evidenced by the discovery that certain sequential patterns occurred more frequently in one group of patients than the other. Conclusion: Given the significant effects shown by our data analyses, we believe that design and implementation of automated clinical decision support systems to constantly monitor patients’ medication transactions (and the sequence in which they are administered) and make appropriate alerts to prevent certain possible ADRs, may decrease ADR occurrences and save lives and money.

Original languageEnglish
Pages (from-to)62-70
Number of pages9
JournalInternational Journal of Medical Informatics
StatePublished - 1 May 2019
Externally publishedYes


  • Adverse drug events
  • Adverse drug reactions
  • Electronic health records
  • Emergent pattern mining
  • Prescriptions sequence


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