Intensive Care Unit Mortality

Predicting Intensive Care Unit (ICU) mortality is crucial for optimizing patient care and resource allocation. Current research heavily focuses on developing accurate predictive models using diverse data sources, including physiological measurements, clinical notes, radiology reports, and images, leveraging machine learning techniques such as deep learning (including LSTM and convolutional neural networks), and federated learning to address data privacy concerns. These models aim to improve the accuracy and timeliness of mortality risk assessment, ultimately leading to better treatment strategies and potentially reducing mortality rates. Furthermore, studies are increasingly investigating the impact of social determinants on model performance and the potential for algorithmic bias.