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Updated: Jan 20, 2026
Autologous Blood Injection to Model Spontaneous Intracerebral Hemorrhage in Mice
Published on: August 24, 2011
To develop a machine learning-based model for predicting the risk of gastrointestinal bleeding in patients with
Chenzhu Cai1, Jiayin Wang2, Mingfa Cai1
1Department of Neurosurgery, Jinjiang Municipal Hospital (Shanghai Sixth People's Hospital Fujian), Jinjiang, Fujian, China.
Insights
Machine learning accurately predicts gastrointestinal bleeding (GIB) in spontaneous intracerebral hemorrhage (sICH) patients. Key predictors include Glasgow Coma Scale score and intraventricular hemorrhage, aiding early risk identification.
Area of Science:
- Neurology
- Gastroenterology
- Medical Informatics
Background:
- Spontaneous intracerebral hemorrhage (sICH) is a severe condition with high mortality.
- Gastrointestinal bleeding (GIB) is a frequent and serious complication in sICH patients.
- Limited research exists on predicting GIB risk in sICH patients.
Purpose of the Study:
- To develop and validate a machine learning model for predicting GIB risk in sICH patients.
- To identify key clinical factors associated with GIB in sICH.
- To provide a decision support tool for early identification of high-risk patients.
Main Methods:
- Retrospective analysis of 738 sICH patients from two centers.
- Feature selection using Boruta and Information-Gain methods, with collinearity checks.
- Model development with Extra Trees Classifier, validated internally and externally using SHAP for interpretability.
Main Results:
- Significant predictors identified: Glasgow Coma Scale score, intraventricular hemorrhage, surgeries, albumin, and midline distance.
- The predictive model achieved an AUC of 0.803 (internal) and 0.757 (external validation).
- Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility.
Conclusions:
- The developed machine learning model demonstrates reliable predictive power for GIB in sICH patients.
- This tool can assist clinicians in the early identification of sICH patients at high risk for GIB.
- The model aids in proactive management and potentially improves patient outcomes.
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 identified as significant predictors of GIB in patients with sICH. The patients were randomly divided into training and validation cohorts in an 8:2 ratio for model development and validation. An Extra Trees Classifier algorithm was used to construct the predictive model. Internal validation showed that the area under the receiver operating characteristic (ROC) curve (AUC) was 0.803 (95% CI: 0.659-0.947), while the AUC for external validation data was 0.757 (95% CI: 0.675-0.839). The calibration curves for both internal and external validation were close to the ideal diagonal line, and decision curve analysis (DCA) demonstrated that the model provided a substantial net benefit.
Conclusion:
Our prediction model for GIB in sICH patients has reliable predictive power and provides a reliable tool for clinicians to identify early the high-risk group for GIB in sICH patients.
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