AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP

In the healthcare industry, the ever-increasing volume of clinical trial data presents challenges for ensuring drug safety and detecting adverse drug reactions (ADRs). This study aims to address the challenge of accurately detecting Serious Adverse Events (SAEs) in pharmacovigilance, a critical component in ensuring drug safety during and after clinical trials. The key problem lies in the underreporting and delayed detection of Adverse Drug Reactions (ADRs) due to the heterogeneous nature of medical data, class imbalance, and the limited scope of traditional monitoring techniques. This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. The objective is to outperform traditional signal detection methods and provide interpretable predictions to aid clinicians in real-time. By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. Through meticulous feature engineering and the application of techniques to address data imbalance, our model demonstrates improved accuracy and interpretability in predicting ADRs. The CNN model achieved an accuracy of 85 %, outperforming traditional models, such as Logistic Regression (78 %) and Support Vector Machines (80 %). These findings suggest that specific demographic and clinical factors significantly influence the likelihood of adverse reactions, offering valuable insights for targeted monitoring and risk mitigation strategies[11]. This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes.•The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. BERT and GPT, were also employed to provide the functionality of textual interactions over medical data.•Performance metrics such as accuracy, precision, recall, and F1-score were systematically applied to evaluate each model's performance. Among the models tested, the CNN model with BERT achieved the highest accuracy, providing valuable insights into the potential of deep learning for enhancing pharmacovigilance practices.•These findings suggest that an inclusion of diverse clinical data when supplied to advanced ML and NLP techniques can significantly improve the detection of ADRs, leading to better alignment with the fundamental principles of Good Clinical Practice (GCP).