Machine Learning for Drug Adverse Event Discovery

We can use unsupervised machine learning to identify which drugs are associated with which adverse events. Specifically, machine learning can help us to create clusters based on gender, age, outcome of adverse event, route drug was administered, purpose the drug was used for, body mass index, etc. This can help for quickly discovering hidden associations between drugs and adverse events. Clustering is a non-supervised learning technique which has wide applications. Some examples where clustering is commonly applied are market segmentation, social network analytics, and astronomical data analysis. Clustering is grouping of data into sub-groups so that objects within a cluster have high similarity in comparison to other objects in that cluster, but are very dissimilar to objects in other classes. For clustering, each pattern is represented as a vector in…


Link to Full Article: Machine Learning for Drug Adverse Event Discovery