Predicting outcome of patients with prolonged disorders of consciousness using machine learning models based on medical complexity.

ABSTRACT: Patients with severe acquired brain injury and prolonged disorders of consciousness (pDoC) are characterized by high clinical complexity and high risk to develop medical complications. The present multi-center longitudinal study aimed at investigating the impact of medical complications on the prediction of clinical outcome by means of machine learning models. Patients with pDoC were consecutively enrolled at admission in 23 intensive neurorehabilitation units (IRU) and followed-up at 6 months from onset via the Glasgow Outcome Scale-Extended (GOSE). Demographic and clinical data at study entry and medical complications developed within 3 months from admission were collected. Machine learning models were developed, targeting neurological outcomes at 6 months from brain injury using data co

SUBMITTER: Liuzzi P 

PROVIDER: S-EPMC9356130 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature