Year Specific Mortality Surface

Year-specific mortality surface research aims to model and predict mortality rates across different ages and years, often leveraging large datasets of demographic, clinical, and socioeconomic information. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs, such as LSTMs), along with ensemble methods like random forests and gradient boosting, to improve prediction accuracy and interpretability. These advancements offer significant potential for improving healthcare resource allocation, risk stratification for personalized interventions, and informing public health policies by providing more accurate and timely mortality predictions.