To develop a machine learning-based model for predicting the risk of gastrointestinal bleeding in patients with spontaneous intracerebral hemorrhage

作者：Chenzhu Cai, Jiayin Wang, Mingfa Cai, Zhen Qi, Xieli Guo · 发表于：Frontiers in Neurology · 年份：2026 · DOI：10.3389/fneur.2025.1690638 · 研究领域：Intracerebral and Subarachnoid Hemorrhage Research、Gastrointestinal Bleeding Diagnosis and Treatment、Nosocomial Infections in ICU

Background: Spontaneous intracerebral hemorrhage (sICH) is a critical illness with a poor clinical prognosis, and gastrointestinal bleeding (GIB) is a severe complication that can significantly worsen the patient's adverse outcomes. However, research on the risk factors for GIB in sICH patients is currently limited. Therefore, this study aims to construct and validate a predictive model for GIB risk in sICH patients using machine learning methods, providing decision support for the early identification of high-risk patients in clinical settings. Methods: The present study retrospectively analysed the clinical data of 738 patients with sICH from two centres. In the feature selection process, the Boruta algorithm was initially employed for preliminary screening, and subsequently, the Information-Gain method was utilised to identify significant predictors. Following this, Spearman correlation analysis was implemented to eliminate collinearity between variables. During the model construction stage, the machine learning algorithm was optimized based on the internal test set, and the model performance was finally verified by the internal test set and the external validation set. In order to enhance the interpretability of the model, the SHapley Additive exPlanations (SHAP) method was used to visualize the prediction results. Results: The Glasgow Coma Scale (GCS) score, intraventricular extension of hemorrhage (ICH with IVH), surgeries, albumin, and distance to the midline were iden...