Peng, MingkaiSundararajan, VijayaWilliamson, TylerMinty, Evan P.Smith, Tony C.Doktorchik, Chelsea T.A.Quan, Hude2018-05-1720182018-05-172018Peng, M., Sundararajan, V., Williamson, T., Minty, E. P., Smith, T. C., Doktorchik, C. T. A., & Quan, H. (2018). Data on coding association rules from an inpatient administrative health data coded by International classification of disease - 10th revision (ICD-10) codes. Data in Brief, 18, 710–712. https://doi.org/10.1016/j.dib.2018.02.043https://hdl.handle.net/10289/11839Data presented in this article relates to the research article entitled “Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data” (Peng et al. [1]) in preparation). We provided a set of ICD-10 coding association rules in the age group of 55 to 65. The rules were extracted from an inpatient administrative health data at five acute care hospitals in Alberta, Canada, using association rule mining. Thresholds of support and confidence for the association rules mining process were set at 0.19% and 50% respectively. The data set contains 426 rules, in which 86 rules are not nested. Data are provided in the supplementary material. The presented coding association rules provide a reference for future researches on the use of association rule mining for data quality assessment.application/pdfen© 2018 Published by Elsevier Inc. This is an open access article under the CC BY license.computer scienceMachine learningJournal Article10.1016/j.dib.2018.02.0432352-3409