The Role of Artificial Intelligence in Reducing the Risk of Adverse Reactions in Multiple Drug Interactions

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Abstract

The available data on the role of polypragmasia in increasing the frequency of multiple drug interactions, when one drug interacts with two or more other drugs, increasing the risk of side effects associated with them, are considered. The application of network analysis and artificial intelligence to predict the development of clinically significant adverse reactions in conditions of polypharmacotherapy is described. The mechanisms of pharmacodynamic and pharmacokinetic interaction of drugs in the development of adverse reactions are considered and drugs potentially carrying an increased risk in multiple drug interactions are noted. The most dangerous drugs involved in drug interactions were psychotropic drugs, which accounted for about a third of all applicable medicines. The most common serious potential complications associated with this interaction were serotonin syndrome, seizures, QT prolongation, and bleeding. Graph probabilistic models, machine learning models for analyzing reliable sources of medical data, factor models that allow assessing the risks of taking two or more drugs together are proposed. These models are implemented in software and can be implemented in clinical decision support systems. It is concluded that the use of artificial intelligence can reduce the risk of adverse reactions during polypharmacotherapy, especially in elderly patients.

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About the authors

Nikolay L. Shimanovsky

Plekhanov Russian University of Economics; Pirogov Russian National Search Medical University

Author for correspondence.
Email: shimann@yandex.ru
ORCID iD: 0000-0001-8887-4420
SPIN-code: 5232-8230

MD, PhD, Professor, Corresponding Member of the RAS

Россия, Moscow; Moscow

Vladimir A. Sudakov

Plekhanov Russian University of Economics

Email: sudakov@ws-dss.com
ORCID iD: 0000-0002-1658-1941
SPIN-code: 1614-4760

PhD in Technical Sciences, Associate Professor

Россия, Moscow

Valery V. Beregovykh

Russian Academy of Sciences

Email: beregovykh@ramn.ru
ORCID iD: 0000-0002-0210-4570
SPIN-code: 5940-7554

PhD in Technical Sciences, Professor, Academician of the RAS

Россия, Moscow

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