English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Sepsis is a poorly understood and potentially life-threatening complicationthat can occur as a result of infection. Early detection and treatment improvespatient outcomes, and as such it poses an important challenge in medicine. Inthis work, we develop a flexible classifier that leverages streaming labresults, vitals, and medications to predict sepsis before it occurs. We modelpatient clinical time series with multi-output Gaussian processes, maintaininguncertainty about the physiological state of a patient while also imputingmissing values. The mean function takes into account the effects of medicationsadministered on the trajectories of the physiological variables. Latentfunction values from the Gaussian process are then fed into a deep recurrentneural network to classify patient encounters as septic or not, and the overallmodel is trained end-to-end using back-propagation. We train and validate ourmodel on a large dataset of 18 months of heterogeneous inpatient stays from theDuke University Health System, and develop a new "real-time" validation schemefor simulating the performance of our model as it will actually be used. Ourproposed method substantially outperforms clinical baselines, and improves on aprevious related model for detecting sepsis. Our model's predictions will bedisplayed in a real-time analytics dashboard to be used by a sepsis rapidresponse team to help detect and improve treatment of sepsis.

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