Machine Learning Predictive Models for Sepsis Risk in ICU Patients With Intracerebral Hemorrhage

* Data Collection: This study utilized two public databases. The model leveraged clinical data obtained from the Medical Information Mart for Intensive Care (MIMIC) IV database (version 2.2) and selected corresponding patients for external validation from the eICU Collaborative Research Database (eICU-CRD). Data on ICH patients were extracted from the MIMIC IV public database, including baseline characteristics, clinical parameters, therapeutic interventions, and outcomes. The data were randomly divided into two groups, with 70% serving as the training set and 30% as the validation set. * Model Development: Feature selection was performed using Lasso regression to construct various machine learning models (such as Random Forest, Logistic Regression, and Neural Networks). * Model Validation: In addition to the internal validation set, external validation was also conducted on the eICU database to test the model's generalizability. * Statistical Analysis: The predictive performance of the model was evaluated using metrics including the area under the ROC curve (AUC), sensitivity, and specificity. * Clinical Applicability Assessment: The clinical utility of the model was assessed using Decision Curve Analysis (DCA).