Improved diagnosis-medication association mining to reduce pseudo-associations

Ching-Huan Wang; Phung Anh Nguyen; Yu Chuan Jack Li; Md Mohaimenul Islam; Tahmina Nasrin Poly; Quoc-Viet Tran; Chih-Wei Huang; Hsuan-Chia Yang

doi:10.1016/j.cmpb.2021.106181

Improved diagnosis-medication association mining to reduce pseudo-associations

Comput Methods Programs Biomed. 2021 Aug:207:106181. doi: 10.1016/j.cmpb.2021.106181. Epub 2021 May 16.

Authors

Ching-Huan Wang¹, Phung Anh Nguyen², Yu Chuan Jack Li³, Md Mohaimenul Islam⁴, Tahmina Nasrin Poly⁴, Quoc-Viet Tran⁵, Chih-Wei Huang⁶, Hsuan-Chia Yang⁷

Affiliations

¹ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
² International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Healthcare Information and Management, School of Health Technology, Ming Chuan University, Taipei, Taiwan.
³ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan; TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan.
⁴ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
⁵ Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
⁶ International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
⁷ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan. Electronic address: itpharmacist@gmail.com.

PMID: 34052770
DOI: 10.1016/j.cmpb.2021.106181

Abstract

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.

Keywords: Association rule mining; Disease-medication association; Inappropriate prescription; Pseudo-association; Taiwan database.

MeSH terms

Algorithms*
Taiwan