  Although timely sepsis diagnosis and prompt interventions in Intensive Care
Unit (ICU) patients are associated with reduced mortality, early clinical
recognition is frequently impeded by non-specific signs of infection and
failure to detect signs of sepsis-induced organ dysfunction in a constellation
of dynamically changing physiological data. The goal of this work is to
identify patient at risk of life-threatening sepsis utilizing a data-centered
and machine learning-driven approach. We derive a mortality risk predictive
dynamic Bayesian network (DBN) guided by a customized sepsis knowledgebase and
compare the predictive accuracy of the derived DBN with the Sepsis-related
Organ Failure Assessment (SOFA) score, the Quick SOFA (qSOFA) score, the
Simplified Acute Physiological Score (SAPS-II) and the Modified Early Warning
Score (MEWS) tools.
  A customized sepsis ontology was used to derive the DBN node structure and
semantically characterize temporal features derived from both structured
physiological data and unstructured clinical notes. We assessed the performance
in predicting mortality risk of the DBN predictive model and compared
performance to other models using Receiver Operating Characteristic (ROC)
curves, area under curve (AUROC), calibration curves, and risk distributions.
  The derived dataset consists of 24,506 ICU stays from 19,623 patients with
evidence of suspected infection, with 2,829 patients deceased at discharge. The
DBN AUROC was found to be 0.91, which outperformed the SOFA (0.843), qSOFA
(0.66), MEWS (0.73), and SAPS-II (0.77) scoring tools. Continuous Net
Reclassification Index and Integrated Discrimination Improvement analysis
supported the superiority DBN. Compared with conventional rule-based risk
scoring tools, the sepsis knowledgebase-driven DBN algorithm offers improved
performance for predicting mortality of infected patients in ICUs.
