  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 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. Conclusions: 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.
