The stacking ensemble combining RF, LightGBM, and DNN performed well on internal test sets, exhibiting an NPV greater than 99.9% even with substantial class imbalance. While performance was lower on the external eICU cohort compared to the internal test sets, sensitivity remained robust. Therefore, the stacking ensemble may serve as a rule-out screening option for ERs and ICUs after additional prospective multi-site validation studies for its efficacy in real-world.
@article{arxiv.2510.15218,
title = {Ensemble Deep Learning Models for Early Detection of Meningitis in ICU: Multi-center Study},
author = {Han Ouyang and Ayush Singhal and Jesse Hamilton and Saeed Amal},
journal= {arXiv preprint arXiv:2510.15218},
year = {2026}
}