An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data.
Abstract
Background
This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS).
Methods
We analyzed a retrospective cohort of 5,014 adult AIS patients from three tertiary academic centers (2019-2023). Centers A and B (n=3,512) formed the development cohort; Center C (n=1,502) served as the external validation cohort. Sixty-three predictors across seven domains were extracted from the initial 24 hours. A consensus feature selection approach combining LASSO, RFE-RF, filter methods, and XGBoost importance was employed, and six machine learning algorithms were evaluated using nested cross-validation, Bayesian optimization, SMOTE, and rigorous anti-leakage protocols. SHAP values and a logistic nomogram enhanced interpretability.
Results
The best-performing CatBoost model with 22 RFE-RF features achieved AUC-ROC of 0.917 (95% CI 0.899-0.933) internally and 0.891 (95% CI 0.868-0.912) externally, significantly outperforming APACHE III, SOFA, OASIS, and GCS (ΔAUC 0.142-0.193, all p<0.001). Calibration was excellent (slopes 0.934-0.977, Brier 0.098-0.114). A pre-specified 0-12h sensitivity analysis confirmed predictive validity is not dependent on late-stage trajectories (external AUC 0.873). A 10-feature nomogram achieved external AUC 0.871 (ΔAUC -0.020 vs CatBoost) with superior DCA net benefit over all comparators.
Conclusions
Integration of bedside nursing assessments with emergency and laboratory data into an ensemble gradient-boosting model markedly improves early in-hospital mortality prediction in AIS compared with established ICU scores.