Peng Mingkai, Sundararajan Vijaya, Williamson Tyler, Minty Evan P, Smith Tony C, Doktorchik Chelsea T A, Quan Hude
Department of Community Health Sciences, University of Calgary, Calgary, Canada.
Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia.
Data Brief. 2018 Feb 16;18:710-712. doi: 10.1016/j.dib.2018.02.043. eCollection 2018 Jun.
Data 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.
本文中呈现的数据与一篇正在准备中的研究论文《探索关联规则挖掘在住院患者管理健康数据编码一致性和完整性评估中的应用》（Peng等人[1]）相关。我们提供了一组55至65岁年龄组的ICD - 10编码关联规则。这些规则是从加拿大艾伯塔省五家急症护理医院的住院患者管理健康数据中，通过关联规则挖掘提取出来的。关联规则挖掘过程的支持度和置信度阈值分别设定为0.19%和50%。该数据集包含426条规则，其中86条规则不嵌套。数据在补充材料中提供。所呈现的编码关联规则为未来关于使用关联规则挖掘进行数据质量评估研究提供了参考。
