# SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing

arXiv: https://arxiv.org/abs/2407.16999 · HTML: https://mlsanity.com/paper/2407.16999/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [Robustness via curvature regularization, and vice versa](https://mlsanity.com/paper/1811.09716/) (2018)
- [Adversarial Robustness through Local Linearization](https://mlsanity.com/paper/1907.02610/) (2019)
- [ASAC: Active Sensing using Actor-Critic models](https://mlsanity.com/paper/1906.06796/) (2019)
- [Inverse Active Sensing: Modeling and Understanding Timely Decision-Making](https://mlsanity.com/paper/2006.14141/) (2020)
- [Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks](https://mlsanity.com/paper/1904.04990/) (2019)
- [On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning](https://mlsanity.com/paper/1912.00672/) (2019)

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

- [SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction](https://mlsanity.com/paper/2501.00190/) (2024)
- [Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten](https://mlsanity.com/paper/2508.07458/) (2025)
- [DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification](https://mlsanity.com/paper/2510.12214/) (2025)

## Abstract (arXiv)

Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.