Application of AI in prediction of possible Drug-Drug interactions
In a recent study, researchers have designed an algorithm using artificial intelligence approach that may be able to alert patients and medical professionals about the possible side effects of Drug-Drug interactions that might occur due to the combination of multiple drugs. To create this alert system, researchers used an autoencoder model, which is basically a type of artificial neural network that is designed on how the human brain processes information. This model is capable of processing both unlabeled and labeled data. Traditionally, programmers need to label data from millions of different combinations of possible interactions to produce the result.
Analysis of Drug-Drug interactions followed by adverse reactions are significant in case of clinical perspective as general patients are prescribed multiple drugs for different disease conditions. The more medication a patient takes, the greater is the possibility of drug-drug interactions and ultimately negative side effects that may include long-term organ damage and even death.
The researchers only focused on the interactions those are of high priority with much severe side effects, which may include life-threatening conditions, disability and hospitalization. The data used in the study was compiled by Food and Drug administration Adverse Event Reporting system and of potentially severe drug-drug interactions from the national Coordinator for Health Information Technology. The team also used information from online databases at Drug Bank and Drugs.com. These data included about 2, 891 drugs and more than 1 million drug combinations. A total of 1,740,770 reports were found on serious health outcomes from drug-drug interactions.
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