Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage.

ABSTRACT: Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognostic expectations. We evaluated the performance of a machine learning (ML) model to predict the 6-month functional status in elderly patients with ICH leveraging the predictive value of the clinical characteristics at hospital admission. Data were extracted by a retrospective multicentric database of patients ≥ 70 years of age consecutively admitted for the management of spontaneous ICH between January 1, 2014 and December 31, 2019. Relevant demographic, clinical, and radiological variables were selected by a feature selection algorithm (Boruta)

SUBMITTER: Trevisi G 

PROVIDER: S-EPMC9349060 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature