# Ensemble Deep Learning Models for Early Detection of Meningitis in ICU: Multi-center Study

arXiv: https://arxiv.org/abs/2510.15218 · HTML: https://mlsanity.com/paper/2510.15218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 56 references that are in the map.
- First posted on arXiv 17 Oct 2025.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics](https://mlsanity.com/paper/2005.01575/) (2020)
- [Recent Advances in Predictive Modeling with Electronic Health Records](https://mlsanity.com/paper/2402.01077/) (2024)

## Abstract (arXiv)

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.