Machine learning models for mortality prediction in patients with spontaneous subarachnoid hemorrhage following ICU treatment.

ABSTRACT:

Background

Spontaneous subarachnoid hemorrhage (SAH) is a severe and potentially life-threatening acute cerebrovascular disease. Early identification of the risk of death in patients with spontaneous SAH is of vital importance for improving prognosis, reducing mortality, and guiding clinical treatment.

Methods

A retrospective cohort study was conducted using the public database, Medical Information Mart for Intensive Care IV (MIMIC)-IV. The primary outcome was in-hospital mortality following intensive care unit (ICU) treatment. All features were extracted from first-day ICU admission data. Data analysis was performed by using R and Python, with feature selection conducted via least absolute shrinkage and selection operator (LASSO) regression. We constructed 8 models based on

SUBMITTER: Hu W 

PROVIDER: S-EPMC12485233 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature