Dynamic Bayesian network for predicting physiological changes, organ dysfunctions and mortality risk in critical trauma patients.

ABSTRACT:

Background

Critical trauma patients are particularly prone to increased mortality risk; hence, an accurate prediction of their conditions enables early identification of patients' mortality status. Thus, we aimed to develop and validate a real-time prediction model for physiological changes, organ dysfunctions and mortality risk in critical trauma patients.

Methods

We used Dynamic Bayesian Networks (DBNs) to model complicated relationships of physiological variables across time slices, accessing data of trauma patients from the Medical Information Mart for Intensive Care database (MIMIC-III) (n = 2915) and validated with patients' data from ICU admissions at the Changhai Hospital (ICU-CH) (n = 1909). The DBN model's evaluation included the predictive ability of physiological

SUBMITTER: Chen Q 

PROVIDER: S-EPMC9063308 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature