Dynamic Risk

Dynamic risk assessment focuses on evaluating and predicting risks that change over time, moving beyond static analyses. Current research emphasizes the use of machine learning, particularly deep learning models like artificial neural networks and conditional variational autoencoders, to process complex, real-time data streams for improved risk prediction across diverse fields. Applications range from enhancing safety in autonomous driving systems and improving patient survival prediction in intensive care units to developing more robust financial risk management strategies. This evolving field offers significant potential for improving decision-making and outcomes in various high-stakes domains.