Adverse Reaction Prediction and Pharmacovigilance on Nutraceutical

This is the authors' abstract. We don't add key points for this paper.

The increasing use of nutraceuticals, defined asfood products with health benefits beyond basic nutrition, has raised significant concerns regarding their safety and potential adverse reactions. This study aims to explore predictive models for adverse reactions associated with nutraceutical consumption and emphasizes the critical role of pharmacovigilance in monitoring these effects. We analyse extensive data from clinical trials, post-market surveillance, and adverse event reporting systems to identify common reactions and risk factors (Smith et al., 2022; Johnson & Lee, 2023). By employing advanced machine learning techniques, we develop a robust framework that enhances the prediction of adverse events related to nutraceuticals, facilitating more informed consumer choices and improving regulatory oversight (Doe & Nguyen, 2021; Patel et al., 2023). Our findings reveal that certain demographics and co-medications significantly influence the likelihood of adverse reactions (Williams et al., 2024). Furthermore, we advocate for a structured pharmacovigilance system specifically tailored to nutraceuticals, which is essential for ensuring consumer safety and fostering a comprehensive understanding of interactions between nutraceuticals and conventional medications (Martinez et al., 2024; Chen & Zhao, 2022). This study contributes to the growing body of literature on nutraceutical safety, highlighting the urgent need for enhanced monitoring and regulatory frameworksin this rapidly expanding sector.