Development of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients.

ABSTRACT: Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned admission to the intensive care unit (ICU). Classical severity scores have been developed typically using relatively few patient features. Recently, deep learning-based models demonstrated better individualized risk assessments compared to classic risk scores, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-stamped data from electronic health records. We developed a deep learning model based on embedded text from multiple data sources and recurrent ne

SUBMITTER: Placido D 

PROVIDER: S-EPMC10256150 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature