Drug Safety

Drug safety research aims to identify and mitigate adverse drug reactions, improving patient outcomes and public health. Current efforts focus on leveraging machine learning, particularly deep learning architectures like recurrent neural networks (RNNs) and convolutional neural networks (CNNs), along with natural language processing (NLP) techniques, to analyze diverse data sources such as electronic health records, clinical trial data, and social media. These computational approaches accelerate pharmacovigilance, enabling faster detection of safety signals and more efficient assessment of drug toxicity, ultimately enhancing the speed and accuracy of drug development and post-market surveillance.