Application of Projected Gradient Methods in Survival Analysis in Public Health

This article analyzes the use of projected gradient methods in survival analysis within the field of public health, highlighting their ability to handle complex data and multiple censoring conditions. Based on a systematic review of 15 studies selected through the Scopus database, their effectiveness in modeling epidemiological risks and predicting clinical outcomes in high-dimensional environments was demonstrated. The integration of advanced technologies, such as recurrent neural networks, optimizers like Adam, and data augmentation techniques through GANs, has allowed overcoming challenges associated with data scarcity and imbalance, significantly improving the quality of the analyses. Additionally, the use of hybrid models, combining traditional approaches with innovative methodologies, has expanded the possibilities for prediction in complex scenarios. This work concludes that projected gradient methods, along with modern tools, are essential for designing more effective public health policies and prevention strategies. The need to continue investing in technological infrastructure and training is emphasized to maximize their applicability in the field of public health.