Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.
Journal:
CNS neuroscience & therapeutics
PMID:
33249760
Abstract
AIMS: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms.