Advancing Pharmacovigilance: AI and Deep Learning Approaches to Detect Adverse Drug Reactions from Big Data

Abstract

The systematic monitoring of post-marketing drug safety is paramount to ensuring patient welfare and population-level health, requiring the timely detection and prevention of adverse drug events. Traditional pharmacovigilance schemes, which largely rely on spontaneous reporting, have weaknesses, including underreporting, delays in the detection of signals, and a lack of cohesion in data sources. As heterogeneous health data grows exponentially, conventional methods are becoming less effective at providing complete, real-time ADR monitoring. This study examined the potential of improved computational methods, particularly artificial intelligence (AI) and deep learning, to enhance pharmacovigilance practices. Neural network architectures, including Bidirectional Encoder Representations with Transformers (BERT), Long Short-term Memory (LSTM), and Convolutional Neural Networks (CNNs), are capable of detecting meaningful patterns in unstructured and complex data. Natural language processing methods have been identified as capable of interpreting free-text clinical narratives, PROs, and biomedical literature relevant to drug safety monitoring. A wide range of data environments has been studied, including electronic health records, global safety-reporting schemes, the scientific literature, and patient communities on the Internet. The commonly used performance evaluation metrics were also reviewed in this study to assess the model's strength and reliability. The feasibility of such technologies in practice can be seen in the field of operation of these robots, such as automated ADR detection using the FDA Adverse Event Reporting System (FAERS) and early signal detection via social media mining. These results indicate the potential for substantial improvements in current pharmacovigilance systems through the use of AI-based models that can identify latent relationships and deliver safety alerts in near-real time. Nevertheless, there are still problematic areas, including data heterogeneity, lack of standardization, algorithmic bias, low interpretability, and ethical and regulatory issues. This work highlights the need to ensure the integration of AI in pharmacovigilance by collaborating with other disciplines and revising regulatory guidelines. Overall, this research offers practical recommendations for the development of data-driven surveillance of drug safety in recent healthcare systems.