Challenges in utilizing the FAERS database for adverse drug reaction data mining: A critical analysis

The FAERS database is a vital tool for identifying adverse drug reactions (ADRs). However, data mining in FAERS faces significant challenges, including data quality issues (e.g., integrity, consistency, and completeness) and limitations in traditional model selection. These issues can introduce biases and affect the reliability of safety signal detection. This review critically analyzes the current state and limitations of FAERS data mining, particularly by briefly comparing it with other mainstream global databases to contextualize its unique challenges. It then proposes optimization strategies, focusing on improved data preprocessing, algorithm refinement, and the integration of emerging technologies. We emphasize the potential of Artificial Intelligence (AI) and multi-source data fusion to enhance detection sensitivity, accelerate the risk signal identification cycle, and address challenges in data-limited scenarios, such as rare diseases. We recommend promoting database standardization, strengthening validation, and formulating policy changes to fully realize FAERS's potential for precision pharmacovigilance.