  The timeliness of detection of a sepsis event in progress is a crucial factor
in the outcome for the patient. Machine learning models built from data in
electronic health records can be used as an effective tool for improving this
timeliness, but so far the potential for clinical implementations has been
largely limited to studies in intensive care units. This study will employ a
richer data set that will expand the applicability of these models beyond
intensive care units. Furthermore, we will circumvent several important
limitations that have been found in the literature: 1) Models are evaluated
shortly before sepsis onset without considering interventions already
initiated. 2) Machine learning models are built on a restricted set of clinical
parameters, which are not necessarily measured in all departments. 3) Model
performance is limited by current knowledge of sepsis, as feature interactions
and time dependencies are hardcoded into the model. In this study, we present a
model to overcome these shortcomings using a deep learning approach on a
diverse multicenter data set. We used retrospective data from multiple Danish
hospitals over a seven-year period. Our sepsis detection system is constructed
as a combination of a convolutional neural network and a long short-term memory
network. We suggest a retrospective assessment of interventions by looking at
intravenous antibiotics and blood cultures preceding the prediction time.
Results show performance ranging from AUROC 0.856 (3 hours before sepsis onset)
to AUROC 0.756 (24 hours before sepsis onset). We present a deep learning
system for early detection of sepsis that is able to learn characteristics of
the key factors and interactions from the raw event sequence data itself,
without relying on a labor-intensive feature extraction work.
