# An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection

arXiv: https://arxiv.org/abs/1708.05894 · HTML: https://mlsanity.com/paper/1708.05894/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2017.

## Built on by (most cited first, 16 of 16)

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- [NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients](https://mlsanity.com/paper/2304.12737/) (2023)
- [ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data](https://mlsanity.com/paper/2212.06364/) (2022)
- [Temporal Sepsis Modeling: a Relational and Explainable-by-Design Framework](https://mlsanity.com/paper/2601.21747/) (2026)
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## Abstract (arXiv)

Sepsis is a poorly understood and potentially life-threatening complication that can occur as a result of infection. Early detection and treatment improves patient outcomes, and as such it poses an important challenge in medicine. In this work, we develop a flexible classifier that leverages streaming lab results, vitals, and medications to predict sepsis before it occurs. We model patient clinical time series with multi-output Gaussian processes, maintaining uncertainty about the physiological state of a patient while also imputing missing values. The mean function takes into account the effects of medications administered on the trajectories of the physiological variables. Latent function values from the Gaussian process are then fed into a deep recurrent neural network to classify patient encounters as septic or not, and the overall model is trained end-to-end using back-propagation. We train and validate our model on a large dataset of 18 months of heterogeneous inpatient stays from the Duke University Health System, and develop a new "real-time" validation scheme for simulating the performance of our model as it will actually be used. Our proposed method substantially outperforms clinical baselines, and improves on a previous related model for detecting sepsis. Our model's predictions will be displayed in a real-time analytics dashboard to be used by a sepsis rapid response team to help detect and improve treatment of sepsis.