In critical care, intensivists are required to continuously monitor high dimensional vital signs
and lab measurements to detect and diagnose acute patient conditions. This has always …

Background. In critical care, intensivists are required to continuously monitor high-
dimensional vital signs and lab measurements to detect and diagnose acute patient …

Deep learning models are increasingly studied in the field of critical care. However, due to
the lack of external validation and interpretability, it is difficult to generalize deep learning …

Progress of machine learning in critical care has been difficult to track, in part due to
absence of public benchmarks. Other fields of research (such as vision and NLP) have …

Progress of machine learning in critical care has been difficult to track, in part due to
absence of public benchmarks. Other fields of research (such as computer vision and …

Background A real-time model for predicting short-term mortality in critically ill patients is
needed to identify patients at imminent risk. However, the performance of the model needs …

A large number of patient healthcare data have been collected in the process of diagnosis
and treatment of intensive care medicine, which provides major benefits for patient safety …

Background The rapid development in big data analytics and the data-rich environment of
intensive care units together provide unprecedented opportunities for medical …

With increasing hospital adoption of electronic health record (EHR) systems worldwide, a
massive amount of EHR data are generated in intensive care practice, and deep learning …

Background: The requirement of mechanical ventilation has increased in recent years.
Patients in the intensive care unit (ICU) who undergo mechanical ventilation often …