Mortality Prediction

Mortality prediction research aims to develop accurate models for estimating the likelihood of death within specific timeframes, leveraging diverse data sources to improve healthcare decision-making. Current research heavily utilizes machine learning algorithms, particularly tree-based methods (like XGBoost, Random Forest) and deep learning architectures, often incorporating both structured clinical data (e.g., lab results, vital signs) and unstructured data (e.g., clinical notes, images) to enhance predictive power. These advancements hold significant potential for optimizing resource allocation, personalizing treatment strategies, and improving patient outcomes across various medical conditions, although challenges remain in addressing data imbalances and ensuring model reliability and interpretability.