Background and Objective: Association rule mining has been adopted to medical fields to discover prescribing patterns or relationships among diseases and/or medications; however, it has generated unreasonable associations among these entities. This study aims to identify the real-world profile of disease-medication (DM) associations using the modified mining algorithm and assess its performance in reducing DM pseudo-associations. Methods: We retrieved data from outpatient records between January 2011 and December 2015 in claims databases maintained by the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan. The association rule mining's lift (Q-value) was adopted to quantify DM associations, referred to as Q1 for the original algorithm and as Q2 for the modified algorithm. One thousand DM pairs with positive Q1-values (Q1+) and negative or no Q2-values (Q2− or Q2∅) were selected as the validation dataset, in which two pharmacists assessed the DM associations. Results: A total of 3,120,449 unique DM pairs were identified, of which there were 333,347 Q1+Q2− pairs and 429,931 Q1+Q2∅ pairs. Q1+Q2− rates were relatively high in ATC classes C (29.91%) and R (30.24%). Classes L (69.91%) and V (52.52%) demonstrated remarkably high Q1+Q2∅ rates. For the 1000 pairs in the validation, 93.7% of the Q1+Q2− or Q1+Q2∅ DM pairs were assessed as pseudo-associations. However, classes M (5.3%), H (4.5%), and B (4.1%) showed the highest rates of plausible associations falsely given Q2− or Q2∅ by the modified algorithm. Conclusions: The modified algorithm demonstrated high accuracy to identify pseudo-associations regarded as positive associations by the original algorithm and would potentially be applied to improve secondary databases to facilitate research on real-world prescribing patterns and further enhance drug safety.
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