Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks.

ABSTRACT:

Background

Accurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on medical expenditure and subsequent disposition. This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time of hospitalization.

Methods

We retrieved the medical records of patients who received acute ischemic stroke diagnoses and were treated at a stroke center between January 2016 and June 2020, and a retrospective analysis of these data was performed. Prolonged length of stay was defined as a hospital stay longer than the median number of days. We applied artificial neural networks to derive prediction models using parameters associated with the leng

SUBMITTER: Yang CC 

PROVIDER: S-EPMC9947790 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature