Advanced Certificate in Predictive Analytics for Drug Safety
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⢠Advanced Statistical Modeling: This unit covers various advanced statistical models, including regression, time series analysis, and multivariate analysis, used in predictive analytics for drug safety.
⢠Machine Learning Techniques: This unit delves into machine learning techniques, such as decision trees, random forests, and neural networks, for predicting adverse drug reactions and enhancing drug safety.
⢠Pharmacovigilance and Signal Detection: This unit focuses on pharmacovigilance and signal detection methods, including data mining and disproportionality analysis, for identifying safety issues in drugs.
⢠Big Data Analytics in Drug Safety: This unit explores the role of big data analytics in drug safety, including data management, data visualization, and real-world data analysis.
⢠Natural Language Processing (NLP) in Drug Safety: This unit covers the use of NLP techniques for analyzing and extracting information from unstructured data, such as medical literature and social media, to enhance drug safety.
⢠Safety Assessment in Clinical Trials: This unit focuses on safety assessment strategies in clinical trials, including sample size calculation, data monitoring, and interim analysis.
⢠Real-World Data Analysis for Post-Marketing Surveillance: This unit explores the use of real-world data, such as electronic health records and claims data, for post-marketing surveillance and safety analysis.
⢠Pharmacoepidemiology and Causal Inference: This unit covers the principles of pharmacoepidemiology and causal inference, including observational studies, bias, and confounding, in drug safety analysis.
⢠Risk Management and Communication: This unit focuses on risk management strategies and communication techniques for addressing safety issues and communicating risks to stakeholders.
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