Machine Learning on adverse drug reactions for Pharmacovigilance

Download Type: External Link

The application of machine learning in pharmacovigilance

 Adverse drug reactions are an unresolved issue that can result in mortality, morbidity and substantial healthcare costs. Many conventional machine learning methods have been used for predicting post-marketing drug side-effects. However, owing to the complex chemical structures of certain drugs and the nonlinear and imbalanced nature of biological data, some side-effects might not be detected. Motivated by the drug discovery research studies that have shown that deep learning outperformed machine learning methods over prediction tasks, we proposed: (i) to exploit the unsupervised deep learning approaches to predict ADRs; (ii) to use a two-stage framework to predict personalized ADRs and repurpose the drugs. This work demonstrates that the proposed framework shows promise in providing more-accurate prediction of side-effects and drug repurposing.

Share this download

More services