GRM-06-150 Comparative Analysis of Deep Learning Architectures for Inpatient Mortality Prediction Using Time-Series Vital Signs

GRM-06-150 Comparative Analysis of Deep Learning Architectures for Inpatient Mortality Prediction Using Time-Series Vital Signs

https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php

This project evaluates deep learning architectures for predicting inpatient mortality using time-series vital signs derived from the MIMIC-III dataset. A baseline Long Short-Term Memory (LSTM) model was reproduced and extended with Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and Transformer architectures. Vital signs including heart rate, respiratory rate, temperature, and systolic blood pressure were processed into fixed-length time windows for model input. Results indicate that GRU achieved the highest performance, while Transformer models underperformed due to dataset limitations. This study demonstrates the impact of model architecture on clinical time-series prediction tasks.