Browsing by Author "Hariharan, Sanjay"

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    An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection
    (2017-08-19) Futoma, Joseph; Hariharan, Sanjay; Sendak, Mark; Brajer, Nathan; Clement, Meredith; Bedoya, Armando; O'Brien, Cara; Heller, Katherine
    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.
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    Real-Time Sepsis Prediction using an End-to-End Multi Task Gaussian Process RNN Classifier
    (2017) Hariharan, Sanjay

    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 encounter 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 several approximations scale the computations associated with the Gaussian process in a manner so that the entire system can be trained discriminatively 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 33\% and 195\% improved areas under the Receiver Operating Characteristic and Precision Recall curves as compared to the NEWS score currently in use on our own hospital wards.