Artificial intelligence to predict individualized outcome of acute ischemic stroke patients: The SIBILLA project.

ABSTRACT:

Introduction

Formulating reliable prognosis for ischemic stroke patients remains a challenging task. We aimed to develop an artificial intelligence model able to formulate in the first 24 h after stroke an individualized prognosis in terms of NIHSS.

Patients and methods

Seven hundred ninety four acute ischemic stroke patients were divided into a training (597) and testing (197) cohort. Clinical and instrumental data were collected in the first 24 h. We evaluated the performance of four machine-learning models (Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost) in predicting NIHSS at discharge both in terms of variation between discharge and admission (regressor approach) and in terms of severity class namely NIHSS 0-5, 6-10, 11-20, >20 (classifier ap

SUBMITTER: Caliandro P 

PROVIDER: S-EPMC11569556 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature