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Sepsis, a dysregulated immune system response to infection, is among theleading causes of morbidity, mortality, and cost overruns in the Intensive CareUnit (ICU). Early prediction of sepsis can improve situational awarenessamongst clinicians and facilitate timely, protective interventions. While theapplication of predictive analytics in ICU patients has shown early promisingresults, much of the work has been encumbered by high false-alarm rates.Efforts to improve specificity have been limited by several factors, mostnotably the difficulty of labeling sepsis onset time and the low prevalence ofseptic-events in the ICU. Here, we present DeepAISE (Deep ArtificialIntelligence Sepsis Expert), a recurrent neural survival model for the earlyprediction of sepsis. We show that by coupling a clinical criterion fordefining sepsis onset time with a treatment policy (e.g., initiation ofantibiotics within one hour of meeting the criterion), one may rank therelative utility of various criteria through offline policy evaluation. Giventhe optimal criterion, DeepAISE automatically learns predictive featuresrelated to higher-order interactions and temporal patterns among clinical riskfactors that maximize the data likelihood of observed time to septic events.DeepAISE has been incorporated into a clinical workflow, which providesreal-time hourly sepsis risk scores. A comparative study of four baselinemodels indicates that DeepAISE produces the most accurate predictions (AUC=0.90and 0.87) and the lowest false alarm rates (FAR=0.20 and 0.26) in two separatecohorts (internal and external, respectively), while simultaneously producinginterpretable representations of the clinical time series and risk factors.

DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis