Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.

ABSTRACT:

Objective

Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course would provide valuable information for healthcare providers, patients, and families. This study aims to utilize electronic health record (EHR) data and machine learning approaches to predict the adverse outcome for nontraumatic SAH adult patients.

Methods

The cohort included nontraumatic SAH patients treated with vasopressors for presumed DCI from a large EHR database, the Cerner Health Facts^® EMR database (2000-2014). The outcome of interest was the adverse outcome, defined as death in hospital or discharged to hospice

SUBMITTER: Yu D 

PROVIDER: S-EPMC7664270 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature