  Sepsis is a severe medical condition caused by a dysregulated host response
to infection that has a high incidence and mortality rate. Even with such a
high-level occurrence rate, the detection and diagnosis of sepsis continues to
pose a challenge. There is a crucial need to accurately forecast the onset of
sepsis promptly while also identifying the specific physiologic anomalies that
contribute to this prediction in an interpretable fashion. This study proposes
a novel approach to quantitatively measure the difference between patients and
a reference group using non-parametric probability distribution estimates and
highlight when abnormalities emerge using a Jensen-Shannon divergence-based
single sample analysis approach. We show that we can quantitatively distinguish
between these two groups and offer a measurement of divergence in real time
while simultaneously identifying specific physiologic factors contributing to
patient outcomes. We demonstrate our approach on a real-world dataset of
patients admitted to Atlanta, Georgia's Grady Hospital.
