Yanqing Ji1, Hao Ying2, John Tran3, Peter Dews4, See-Yan Lau5, R Michael Massanari6. 1. a Department of Electrical and Computer Engineering , Gonzaga University , Spokane , WA , USA. 2. b Department of Electrical and Computer Engineering , Wayne State University , Detroit , MI , USA. 3. c Frontier Behavioral Health , Spokane , WA , USA. 4. d Department of Medicine , St. Mary Mercy Hospital (Trinity Health) , Livonia , MI , USA. 5. e Department of Pharmacy, Albertsons , Spokane WA , USA. 6. f Research for the Critical Junctures Institute , Bellingham , WA , USA.
Abstract
AIMS: Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on spontaneous reports. These methods suffer from severe underreporting, incompleteness, and various bias. The aim of this study was to more effectively screen potential DDIs using patient electronic data and temporal association mining techniques. METHODS: We focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms. We introduced innovative functional temporal association rules where the degree of temporal association between two events within a patient case was defined by a function. RESULTS: Preliminary test results on two drug pairs (i.e., <benazepril, losartan> and <benazepril, insulin>) were classified into 260 clinically meaningful categories. These categories were evaluated by physicians and the results exhibited that all the potential DDIs were confined to top 20 of the 260 outcomes. CONCLUSIONS: Our methodology can be used to dramatically reduce a long list of association rules to a manageable list for further analysis and investigation by drug safety professionals.
AIMS: Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on spontaneous reports. These methods suffer from severe underreporting, incompleteness, and various bias. The aim of this study was to more effectively screen potential DDIs using patient electronic data and temporal association mining techniques. METHODS: We focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms. We introduced innovative functional temporal association rules where the degree of temporal association between two events within a patient case was defined by a function. RESULTS: Preliminary test results on two drug pairs (i.e., <benazepril, losartan> and <benazepril, insulin>) were classified into 260 clinically meaningful categories. These categories were evaluated by physicians and the results exhibited that all the potential DDIs were confined to top 20 of the 260 outcomes. CONCLUSIONS: Our methodology can be used to dramatically reduce a long list of association rules to a manageable list for further analysis and investigation by drug safety professionals.
Authors: Danai Chasioti; Xiaohui Yao; Pengyue Zhang; Samuel Lerner; Sara K Quinney; Xia Ning; Lang Li; Li Shen Journal: IEEE J Biomed Health Inform Date: 2018-10-08 Impact factor: 5.772