  We study multiple rule-based and machine learning (ML) models for sepsis
detection. We report the first neural network detection and prediction results
on three categories of sepsis. We have used the retrospective Medical
Information Mart for Intensive Care (MIMIC)-III dataset, restricted to
intensive care unit (ICU) patients. Features for prediction were created from
only common vital sign measurements. We show significant improvement of AUC
score using neural network based ensemble model compared to single ML and
rule-based models. For the detection of sepsis, severe sepsis, and septic
shock, our model achieves an AUC of 0.97, 0.96 and 0.91, respectively. Four
hours before the positive hours, it predicts the same three categories with an
AUC of 0.90, 0.91 and 0.90 respectively. Further, we ranked the features and
found that using six vital signs consistently provides higher detection and
prediction AUC for all the models tested. Our novel ensemble model achieves
highest AUC in detecting and predicting sepsis, severe sepsis, and septic shock
in the MIMIC-III ICU patients, and is amenable to deployment in hospital
settings.

Machine Learning;Quantitative Methods;Machine Learning