A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients.

ABSTRACT: The evolution of intracranial pressure (ICP) of critically ill patients admitted to a neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and compromised intracranial space, ICP is affected by a multitude of factors, many of which are monitored on the ICU, but the complexity of the resulting patterns limits their clinical use. This paves the way for new machine learning techniques to assist clinical management of patients undergoing invasive ICP monitoring independent of the underlying disease. An institutional cohort (ICP-ICU) of patients with invasive ICP monitoring (n = 1346) was used to train recurrent machine learning models to predict the occurrence of ICP increases of ≥22 mmHg over a long (>2 h) time period in the upcoming hours. External validatio

SUBMITTER: Schweingruber N 

PROVIDER: S-EPMC9486888 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature