Defining predictors for successful mechanical ventilation weaning, using a data-mining process and artificial intelligence.

ABSTRACT: Mechanical ventilation weaning within intensive care units (ICU) is a difficult process, while crucial when considering its impact on morbidity and mortality. Failed extubation and prolonged mechanical ventilation both carry a significant risk of adverse events. We aimed to determine predictive factors of extubation success using data-mining and artificial intelligence. A prospective physiological and biomedical signal data warehousing project. A 21-beds medical Intensive Care Unit of a University Hospital. Adult patients undergoing weaning from mechanical ventilation. Hemodynamic and respiratory parameters of mechanically ventilated patients were prospectively collected and combined with clinical outcome data. One hundred and eight patients were included, for 135 spontaneous breathing tri

SUBMITTER: Menguy J 

PROVIDER: S-EPMC10665387 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature