Machine learning for early dynamic prediction of functional outcome after stroke.

ABSTRACT:

Background

Prediction of outcome after stroke is critical for treatment planning and resource allocation but is complicated by fluctuations during the first days after onset. We propose a machine learning model that can provide hourly predictions based on the integration of continuous variables acquired within 72 h of hospital admission.

Methods

We analyzed 2492 admissions for ischemic stroke in the Geneva University Hospital from 01.01.2018 to 31.12.2021, amounting to 2'131'752 unique data points. We developed a transformer model that continuously included clinical, physiological, imaging, and biological data recorded within 72 h of admission. This model was trained to generate hourly predictions of mortality and morbidity. Shapley additive explanations were used to identif

SUBMITTER: Klug J 

PROVIDER: S-EPMC11561255 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature