Paper Contents
Abstract
Abstract:Pharmacovigilance is crucial for ensuring the safety of medicines. Machine learning and causal inference paradigms have been increasingly applied to pharmacovigilance to improve the detection and prediction of adverse drug events. This review aims to summarize the current state of machine learning in causal inference for pharmacovigilance. We discuss the data sources used in pharmacovigilance, including spontaneous reporting systems, real-world data, social media, and biomedical literature. We also review machine learning techniques, such as association rule mining, clustering, and neural networks, and causal inference paradigms, including propensity score matching, instrumental variable analysis, and regression discontinuity design. Finally, we highlight challenges and future directions in this field, including data quality and standardization, integration of multiple data sources, and development of more advanced machine learning algorithms
Copyright
Copyright © 2024 Datir Sanket Dilip. This is an open access article distributed under the Creative Commons Attribution License.
