  Sepsis is a syndrome that develops in the body in response to the presence of
an infection. Characterized by severe organ dysfunction, sepsis is one of the
leading causes of mortality in Intensive Care Units (ICUs) worldwide. These
complications can be reduced through early application of antibiotics. Hence,
the ability to anticipate the onset of sepsis early is crucial to the survival
and well-being of patients. Current machine learning algorithms deployed inside
medical infrastructures have demonstrated poor performance and are insufficient
for anticipating sepsis onset early. Recently, deep learning methodologies have
been proposed to predict sepsis, but some fail to capture the time of onset
(e.g., classifying patients' entire visits as developing sepsis or not) and
others are unrealistic for deployment in clinical settings (e.g., creating
training instances using a fixed time to onset, where the time of onset needs
to be known apriori). In this paper, we first propose a novel but realistic
prediction framework that predicts each morning whether sepsis onset will occur
within the next 24 hours using the most recent data collected the previous
night, when patient-provider ratios are higher due to cross-coverage resulting
in limited observation to each patient. However, as we increase the prediction
rate into daily, the number of negative instances will increase, while that of
positive instances remain the same. This causes a severe class imbalance
problem making it hard to capture these rare sepsis cases. To address this, we
propose a nightly profile representation learning (NPRL) approach. We prove
that NPRL can theoretically alleviate the rare event problem and our empirical
study using data from a level-1 trauma center demonstrates the effectiveness of
our proposal.
