A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage.

ABSTRACT:

Introduction

Stroke-associated pneumonia (SAP) is a common complication of stroke that can increase the mortality rate of patients and the burden on their families. In contrast to prior clinical scoring models that rely on baseline data, we propose constructing models based on brain CT scans due to their accessibility and clinical universality.

Methods

Our study aims to explore the mechanism behind the distribution and lesion areas of intracerebral hemorrhage (ICH) in relation to pneumonia, we utilized an MRI atlas that could present brain structures and a registration method in our program to extract features that may represent this relationship. We developed three machine learning models to predict the occurrence of SAP using these features. Ten-fold cross-validation was a

SUBMITTER: Yang G 

PROVIDER: S-EPMC10272424 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature