TY - JOUR
T1 - Improved diagnosis-medication association mining to reduce pseudo-associations
AU - Wang, Ching Huan
AU - Nguyen, Phung Anh
AU - Li, Yu-Chuan
AU - Islam, Md Mohaimenul
AU - Poly, Tahmina Nasrin
AU - Tran, Quoc Viet
AU - Huang, Chih Wei
AU - Yang, Hsuan Chia
N1 - Funding Information:
This research was sponsored in part by the Ministry of Science and Technology, Taiwan [grant number: MOST 109-2222-E-038-002-MY2], the Ministry of Education, Taiwan [grant number: MOE 109-6604-001-400], and Taipei Medical University, Taiwan [grant number: TMU107-AE1-B18].
Publisher Copyright:
© 2021
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - Association rule mining
KW - Disease-medication association
KW - Inappropriate prescription
KW - Pseudo-association
KW - Taiwan database
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U2 - 10.1016/j.cmpb.2021.106181
DO - 10.1016/j.cmpb.2021.106181
M3 - Article
AN - SCOPUS:85107062082
SN - 0169-2607
VL - 207
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 106181
ER -