Machine Learning-Based Pharmacovigilance for Early Detection of Adverse Drug Reaction Signals

Abstract

Pharmacovigilance is an essential component of healthcare systems that focuses on the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs) and other medicine-related problems.¹ Conventional pharmacovigilance primarily relies on spontaneous reporting systems and statistical disproportionality methods, which may be affected by under-reporting, incomplete information, reporting bias, and the increasing volume of safety data.² The rapid development of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and real-world data analytics provides new opportunities for improving the efficiency and sensitivity of pharmacovigilance.³ Machine learning algorithms can analyze large and heterogeneous datasets to identify complex relationships between medicines and adverse events that may not be readily recognized using conventional approaches. Algorithms such as random forest, support vector machine, gradient boosting, logistic regression, neural networks, and deep learning have been investigated for adverse event detection and safety signal identification.⁴,⁵ Recent evidence indicates that ML-based approaches may complement traditional disproportionality methods and facilitate earlier identification and prioritization of potential safety signals.⁶ However, challenges involving data quality, model interpretability, external validation, bias, privacy, and regulatory acceptance remain important. This article discusses the principles, workflow, applications, advantages, limitations, and future potential of ML-based pharmacovigilance for early ADR signal detection.