  Sepsis, a dysregulated immune system response to infection, is among the
leading causes of morbidity, mortality, and cost overruns in the Intensive Care
Unit (ICU). Early prediction of sepsis can improve situational awareness
amongst clinicians and facilitate timely, protective interventions. While the
application of predictive analytics in ICU patients has shown early promising
results, much of the work has been encumbered by high false-alarm rates.
Efforts to improve specificity have been limited by several factors, most
notably the difficulty of labeling sepsis onset time and the low prevalence of
septic-events in the ICU. Here, we present DeepAISE (Deep Artificial
Intelligence Sepsis Expert), a recurrent neural survival model for the early
prediction of sepsis. We show that by coupling a clinical criterion for
defining sepsis onset time with a treatment policy (e.g., initiation of
antibiotics within one hour of meeting the criterion), one may rank the
relative utility of various criteria through offline policy evaluation. Given
the optimal criterion, DeepAISE automatically learns predictive features
related to higher-order interactions and temporal patterns among clinical risk
factors that maximize the data likelihood of observed time to septic events.
DeepAISE has been incorporated into a clinical workflow, which provides
real-time hourly sepsis risk scores. A comparative study of four baseline
models indicates that DeepAISE produces the most accurate predictions (AUC=0.90
and 0.87) and the lowest false alarm rates (FAR=0.20 and 0.26) in two separate
cohorts (internal and external, respectively), while simultaneously producing
interpretable representations of the clinical time series and risk factors.
