Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

ABSTRACT: While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the train

SUBMITTER: Yoshida Y 

PROVIDER: S-EPMC10241931 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature