Optimization of Large Vessel Occlusion Detection in Acute Ischemic Stroke Using Machine Learning Methods.

ABSTRACT: The early detection of large-vessel occlusion (LVO) strokes is increasingly important as these patients are potential candidates for endovascular therapy, the availability of which is limited. Prehospital LVO detection scales mainly contain symptom variables only; however, recent studies revealed that other types of variables could be useful as well. Our aim was to comprehensively assess the predictive ability of several clinical variables for LVO prediction and to develop an optimal combination of them using machine learning tools. We have retrospectively analysed data from a prospectively collected multi-centre stroke registry. Data on 41 variables were collected and divided into four groups (baseline vital parameters/demographic data, medical history, laboratory values, and symptoms). F

SUBMITTER: Tarkanyi G 

PROVIDER: S-EPMC8877679 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature