Abstract
We consider the problem of automatically detecting drug-drug interactions, i.e., the occurrence of an adverse reaction caused by the co-administration of two or more drugs, from reports of suspected cases. This is an important problem because of the health implications that correctly identifying drug-drug interactions has, and because the automatic detection of such cases would enable analysis and monitoring at scale. Current automated methods are based on associations and correlation relationships between datapoints, and thus fail to identify casual relationships. In this context, we propose a novel approach that specifically identifies the combined causes and multiple causes of drug-drug interactions, along with the actual direction of the casual relation. The method is empirically validated on a real-world adverse effect dataset and contrasted against current methods for automatic drug-drug interaction.
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Notes
- 1.
MedicinesComplete published in Pharmaceutical Press and the Royal Pharmaceutical Society:https://www.medicinescomplete.com/mc/alerts/current/drug-interactions.htm.
- 2.
Data sources from Micromedex, Multum and Wolters Kluwer database:https://www.drugs.com/drug_interactions.php.
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Subpaiboonkit, S., Li, X., Zhao, X., Zuccon, G. (2022). Causality Discovery Based on Combined Causes and Multiple Causes in Drug-Drug Interaction. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_5
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