Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

ABSTRACT:

Background

Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real-world settings.

Methods and results

ML-based models were developed to predict in-hospital mortality in 527 patients with ICH using raw brain imaging data from brain computed tomography and clinical data. The models' performances were evaluated using the area under the receiver operating characteristic curves and calibration plots, comparing them with traditional risk scores such as the ICH score and ICH grading scale. Kaplan-Meier curves were used to examine the post-ICH survival rates, stratified by ML-based risk a

SUBMITTER: Matsumoto K 

PROVIDER: S-EPMC11935536 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature