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

Abstract: 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). Ear ly 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 encumbe red 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. 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 clinic al 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 Dee pAISE 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 clinica l time series and risk factors. Introduction Sepsis is a syndromic, life - threatening condition that arises when the body's response to infection injures its own internal organs (1) . Though the condition lacks the same public notoriety as other conditions like heart attacks, 6% of all hospitalized patients in the U nited S tates carry a primary diagnosis of sepsis as compared to 2.5% for the latter (2) . When all hospital deaths are ultimately considered, nearly 35% are attributable to sepsis (2) . This condition stands in stark contrast to heart attacks which have a mortality rate of 2.7 - 9.6% and only cost the US $12.1 billion ann ually, roughly half of the cost of sepsis (3) .