# Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data

arXiv: https://arxiv.org/abs/2407.14211 · HTML: https://mlsanity.com/paper/2407.14211/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural network models and deep learning - a primer for biologists](https://mlsanity.com/paper/1902.04704/) (2019)
- [Effect of a Process Mining based Pre-processing Step in Prediction of the Critical Health Outcomes](https://mlsanity.com/paper/2407.02821/) (2024)
- [Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit](https://mlsanity.com/paper/2302.06274/) (2023)

## Built on by (most cited first, 4 of 4)

- [From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance](https://mlsanity.com/paper/2510.03906/) (2025)
- [Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality](https://mlsanity.com/paper/2408.01612/) (2024)
- [GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing](https://mlsanity.com/paper/2510.20266/) (2025)
- [LLM Encoder vs. Decoder: Robust Detection of Chinese AI-Generated Text with LoRA](https://mlsanity.com/paper/2509.00731/) (2025)

## Abstract (arXiv)

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. 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.