An Approach in Prediction of Medicine Side Effects Using RNN Networks

Adverse Drug Reactions (ADRs) are a critical issue in the field of medicine that demands innovative solutions. Various research has been done using machine learning and deep learning approaches to predict the drug side effects. This study delves deep into the realm of Recurrent Neural Networks (RNNs) to revolutionize the prediction of ADRs associated with medications. The groundbreaking RNN-based model harnesses sequential data from both drug structures and patient characteristics to effectively anticipate potential side effects. Through extensive training on a vast dataset of drug-ADR connections and rigorous evaluation, the developed model has demonstrated remarkable accuracy of 98.95% in predicting previously unseen ADRs. When compared to traditional machine learning methods, The proposed RNN model has showcased its process in capturing the intricate relationships between drugs, patients, and side effects. This pioneering work holds immense promise in enhancing patient safety by proactively identifying potential ADRs prior to drug administration.