BMC medical informatics and decision making
Application of machine learning prediction models for in-hospital mortality in diabetic sepsis patients: a study based on the MIMIC IV database
Par Shen Z, Zhang H et Wang Y.
Contenu en anglais
Résumé
Background
Sepsis complicated by diabetes mellitus represents a significant clinical challenge in intensive care units (ICUs), associated with substantial morbidity and mortality. Accurate prognostication of in-hospital mortality in this complex patient population is crucial for optimizing clinical decision making, resource allocation, and patient management strategies.
Methods
We conducted a retrospective cohort study utilizing data from the Medical Information Mart for Intensive Care (MIMIC-IV) database. The study focused on adult ICU patients with concurrent diagnoses of sepsis and diabetes mellitus. Six machine learning algorithms were evaluated for their predictive performance: Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), XGBoost and LightGBM. Additionally, we assessed the prognostic capability of the Sequential Organ Failure Assessment (SOFA) score. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we employed SHAP (SHapley Additive exPlanations) values to elucidate the relative importance of predictor variables.