# Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier

arXiv: https://arxiv.org/abs/1706.04152 · HTML: https://mlsanity.com/paper/1706.04152/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

## Built on by (most cited first, 30 of 47)

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- [Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble](https://mlsanity.com/paper/2106.10352/) (2021)
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- [Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning](https://mlsanity.com/paper/2107.05230/) (2021)
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- [Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique](https://mlsanity.com/paper/2201.05634/) (2022)
- [Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals](https://mlsanity.com/paper/2302.05702/) (2023)
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- [Optimal Look-back Horizon for Time Series Forecasting in Federated Learning](https://mlsanity.com/paper/2511.12791/) (2025)
- [The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards](https://mlsanity.com/paper/2101.10790/) (2021)
- [Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network](https://mlsanity.com/paper/2102.01147/) (2021)
- [CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data](https://mlsanity.com/paper/2104.03739/) (2021)
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- [Task-Synchronized Recurrent Neural Networks](https://mlsanity.com/paper/2204.05192/) (2022)
- [Unsupervised Probabilistic Models for Sequential Electronic Health Records](https://mlsanity.com/paper/2204.07292/) (2022)

## Abstract (arXiv)

We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiological time series using multitask Gaussian processes, seamlessly accounting for the high uncertainty, frequent missingness, and irregular sampling rates typically associated with real clinical data. The Gaussian process is directly connected to a black-box classifier that predicts whether a patient will become septic, chosen in our case to be a recurrent neural network to account for the extreme variability in the length of patient encounters. We show how to scale the computations associated with the Gaussian process in a manner so that the entire system can be discriminatively trained end-to-end using backpropagation. In a large cohort of heterogeneous inpatient encounters at our university health system we find that it outperforms several baselines at predicting sepsis, and yields 19.4% and 55.5% improved areas under the Receiver Operating Characteristic and Precision Recall curves as compared to the NEWS score currently used by our hospital.