Open AccessGraphic Mining of High‐Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records
Author(s) -
Du L,
Chakraborty A,
Chiang CW,
Cheng L,
Quinney SK,
Wu H,
Zhang P,
Li L,
Shen L
Publication year - 2015
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.59
Subject(s) - myopathy , order (exchange) , drug , computer science , medical record , data mining , medicine , pharmacology , business , finance
We propose to study a novel pharmacovigilance problem for mining directional effects of high‐order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof‐of‐concept study, we analyzed a large electronic medical records database and extracted myopathy‐relevant case control drug co‐occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data‐mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice.