Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation.

ABSTRACT:

Background

Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid-base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters.

Methods

We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our coh

SUBMITTER: Mamandipoor B 

PROVIDER: S-EPMC8102841 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature