Machine Learning in Drug Safety
Machine Learning in Drug Safety
Machine Learning (ML) is increasingly playing a pivotal role in drug safety by enhancing the detection, prediction, and management of adverse drug reactions (ADRs). By analyzing large volumes of clinical data, electronic health records, and patient reports, ML algorithms can identify potential safety signals faster and more accurately than traditional methods. This allows for early intervention and improved risk assessment, ultimately leading to safer drug development and post-market surveillance. Moreover, ML models can be continuously updated, enabling them to adapt to new data and emerging patterns, thus enhancing the overall pharmacovigilance process.
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