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

By Joseph D. Futoma, Sanjay Hariharan, Katherine A. Heller, Mark P. Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien. 2017

142 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Epidemiology, Molecular Biology

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Citations in the corpus

Surgical data science – from concepts toward clinical translation (2020, unknown): “PMLR, pp. 243–254. http:// proceedings.mlr.press/v68/futoma17a.html Gale W, Oakden-Rayner L, Carneiro G, Bradley AP, Palmer LJ, 2018.”
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL (2018, background): “Real-time prediction Several models have been proposed for real-time prediction on irregularly sampled EHR data (Soleimani et al., 2017; Futoma et al., 2017; Hwang et al., 2017; Zhang et al., 2017).”
"The human body is a black box": supporting clinical decision-making with deep learning (2019, methodology): “t sepsis diagnoses and major medical and public health organizations publicly promote distinct disease definitions. The model generates risk scores every hour for every adult patient to detect sepsis [18, 19]. The model extended prior work using recurrent neural networks (RNNs) for clinical event de”
Consistent Estimators for Learning to Defer to an Expert (2020, methodology): “These models are either used as a tool to help the downstream human decision maker: judges relying on algorithmic risk assessment tools (Green & Chen, 2019b) and risk scores being used in the ICU (Futoma et al., 2017), or instead these learning models are solely used to make the final prediction on ”