Abstract
Bayesian Network Probabilistic Graphs have recently been applied to the problem of discovery drug-drug interactions, i.e., the identification of drugs that, when consumed together, produce an unwanted side effect. These methods have the advantage of being explainable: the cause of the interaction is made explicit. However, they suffer from two intrinsic problems: (1) the high time-complexity for computing causation, i.e., exponential; and (2) the difficult identification of causality directions, i.e., it is difficult to identify in drug-drug interactions databases whether a drug causes an adverse effect – or vice versa, an adverse effect causes a drug consumption. While solutions for addressing the causality direction identification exist, e.g., the CARD method, these assume statistical independence between drug pairs considered for interaction: real data often does not satisfy this condition.
In this paper, we propose a novel causality discovery algorithm for drug-drug interactions that goes beyond these limitations: Domain-knowledge-driven Causality Discovery (DCD). In DCD, a knowledge base that contains known drug-side effect pairs is used to prime a greedy drug-drug interaction algorithm that detects the drugs that, when consumed together, cause a side effect. This algorithm resolves the drug-drug interaction discovery problem in \(O(n^2)\) time and provides the causal direction of combined causes and their effect, without resorting to assuming statistical independence of drugs intake. Comprehensive experiments on real-world and synthetic datasets show the proposed method is more effective and efficient than current state-of-the-art solutions, while also addressing a number of drawbacks of current solutions, including the high time complexity, and the strong assumptions regarding real-world data that are often violated.
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Notes
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For example, the Food and Drug Administration (FDA) in the United States has collected this type of data in the FDA Adverse Event Report System (FAERS).
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The causal relationship whose direction is unknown.
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The V-structure property, in fact, may not identify all causality structures in real-world DDICAE data [18], although it can identify the direction in causality discovery.
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The relationship describing the causes that are marginally independent become dependent when their common effect is given.
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MedicinesComplete published in Pharmaceutical Press and the Royal Pharmaceutical Society: https://www.medicinescomplete.com/mc/alerts/current/drug-interactions.htm.
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Data sources from Micromedex, Multum and Wolters Kluwer databases: https://www.drugs.com/drug_interactions.php.
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Acknowledgements
The authors would like to acknowledge Meng Wang from Southeast University, China, and Mingyang Zhong from The University of Queensland, Australia, for their input on the initial stages of this work.
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Appendix I: Terminology used in this Paper
Appendix I: Terminology used in this Paper
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BCC: Bayesian Constraint-based Causality
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CARD: Causal Association Rule Discovery
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CBN: Causal Bayesian Network
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DCD: Domain-knowledge-driven Causality Discovery
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DDICAE: Drug-Drug Interactions Causing Adverse Effects
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DDI: Drug-drug interactions
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FAERS: Food and Drug Administration (FDA) adverse event report system
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Subpaiboonkit, S., Li, X., Zhao, X., Scells, H., Zuccon, G. (2019). Causality Discovery with Domain Knowledge for Drug-Drug Interactions Discovery. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_46
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