Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data

Abstract

Background Stroke is second-leading cause of disability and death among adults. Approximately 17 million people suffer from a stroke annually, with about 85% being ischemic strokes. Predicting mortality of ischemic stroke patients in intensive care unit (ICU) is crucial for optimizing treatment strategies, allocating resources, and improving survival rates. Methods We acquired data on ICU ischemic stroke patients from MIMIC-IV database, including diagnoses, vital signs, laboratory tests, medications, procedures, treatments, and clinical notes. Stroke patients were randomly divided into training (70%, n=2441), test (15%, n=523), and validation (15%, n=523) sets. To address data imbalances, we applied Synthetic Minority Over-sampling Technique (SMOTE). We selected 30 features for model development, significantly reducing feature number from 1095 used in the best study. We developed a deep learning model to assess mortality risk and implemented several baseline machine learning models for comparison. Results XGB-DL model, combining XGBoost for feature selection and deep learning, effectively minimized false positives. Model's AUROC improved from 0.865 (95% CI: 0.821 - 0.905) on first day to 0.903 (95% CI: 0.868 - 0.936) by fourth day using data from 3,646 ICU mortality patients in the MIMIC-IV database with 0.945 AUROC (95% CI: 0.944-0.947) during training. Although other ML models also performed well in terms of AUROC, we chose Deep Learning for its higher specificity. Conclusion Through enhanced feature selection and data cleaning, proposed model demonstrates a 13% AUROC improvement compared to existing models while reducing feature number from 1095 in previous studies to 30.

Fig 1. Study workflow.

Diagram of the methodologies adopted in this study.

Fig 2. Patient selection.

Flow diagram of the selection process of patients.

Fig 3. Model architecture.

This figure illustrates the neural network structure used, consisting of three hidden layers with 100, 50, and 25 neurons, respectively, each incorporating a dropout rate of 0.5. The model was trained using 30 input features.

Fig 4. Ablation study.

This figure presents the ablation study conducted for this paper. The upper and lower parts of each box plot represent the high and low ranges of the confidence interval, respectively, while the middle point indicates the AUROC.

Fig 5. Feature importance.

Feature importance and ranking based on XGBoost feature extractor.

Fig 6. AUC comparison.

AUC comparison of different classifiers in four days.

Fig 7. SHAP analysis.

SHAP value based on neural network model for the test set.