Abstract
The condition of ICU patients can change rapidly, sometimes leading to critical deterioration. However, little changes in vital signs such as heart rate and blood pressure often precede these rapid declines. By applying time series analysis and machine learning models, we hope to detect these early signals and implement interventions in advance to save more lives.
Document Type
Article
Class Name
Electrical and Systems Engineering Undergraduate Research
Language
English (en)
Date of Submission
5-7-2025
Recommended Citation
Shi, Zihang, "Predicting 72-hours Mortality for MIMIC-Ⅲ Patients: An Ensemble Learning Approach Using XGboost" (2025). Electrical and Systems Engineering Undergraduate and Graduate Research. 46.
https://openscholarship.wustl.edu/eseundergraduate_research/46
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