A dynamic Bayesian network model for predicting organ failure associations without predefining outcomes.

ABSTRACT: Critical care medicine has been a field for Bayesian networks (BNs) application for investigating relationships among failing organs. Criticisms have been raised on using mortality as the only outcome to determine the treatment efficacy. We aimed to develop a dynamic BN model for detecting interrelationships among failing organs and their progression, not predefining outcomes and omitting hierarchization of organ interactions. We collected data from 850 critically ill patients from the national database used in many intensive care units. We considered as nodes the organ failure assessed by a score as recorded daily. We tested several possible DBNs and used the best bootstrapping results for calculating the strength of arcs and directions. The network structure was learned using a hill clim

SUBMITTER: De Blasi RA 

PROVIDER: S-EPMC8081190 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature