# Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis

arXiv: https://arxiv.org/abs/2209.00439 · HTML: https://mlsanity.com/paper/2209.00439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation](https://mlsanity.com/paper/1906.02830/) (2019)
- [Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation](https://mlsanity.com/paper/2203.08757/) (2022)
- [Generalized Negative Correlation Learning for Deep Ensembling](https://mlsanity.com/paper/2011.02952/) (2020)
- [On the Intrinsic Differential Privacy of Bagging](https://mlsanity.com/paper/2008.09845/) (2020)
- [Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction](https://mlsanity.com/paper/1909.09557/) (2019)
- [Privacy-Preserving Training of Tree Ensembles over Continuous Data](https://mlsanity.com/paper/2106.02769/) (2021)
- [Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning](https://mlsanity.com/paper/2107.05230/) (2021)

## Built on by (most cited first, 3 of 3)

- [Validity problems in clinical machine learning by indirect data labeling using consensus definitions](https://mlsanity.com/paper/2311.03037/) (2023)
- [Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection](https://mlsanity.com/paper/2404.16638/) (2024)
- [Deeply Supervised Multi-Task Autoencoder for Biological Brain Age estimation using three dimensional T1-weighted magnetic resonance imaging](https://mlsanity.com/paper/2508.01565/) (2025)

## Abstract (arXiv)

Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee decision. We show that this technique is an ideal fit for machine learning on medical data: First, ensembles are amenable to parallel and asynchronous learning, thus enabling efficient training of patient-specific component neural networks. Second, building on the idea of minimizing generalization error by selecting uncorrelated patient-specific networks, we show that one can build an ensemble of a few selected patient-specific models that outperforms a single model trained on much larger pooled datasets. Third, the non-iterative ensemble combination step is an optimal low-dimensional entry point to apply output perturbation to guarantee the privacy of the patient-specific networks. We exemplify our framework of differentially private ensembles on the task of early prediction of sepsis, using real-life intensive care unit data labeled by clinical experts.