Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.

ABSTRACT:

Background

This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.

Methods

1274 ICH patients who met the specified inclusion and exclusion criteria were analyzed retrospectively in the MIMIC IV 3.0 database. Patients were randomly divided into training, validation, and testing datasets in a ratio of 6:2:2 based on the outcome distribution. Data from the Second Hospital of Lanzhou University were used as an external validation set. This study used LASSO regression and multivariable logistic regression analysis to screen for features. We then employed XGBoost to construct a machine-learning model. The model's performance was evaluated using ROC curve analysis, calibration curve analysis, clinical decision c

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11755980 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature