  For highly sensitive real-world predictive analytic applications such as
healthcare and medicine, having good prediction accuracy alone is often not
enough. These kinds of applications require a decision making process which
uses uncertainty estimation as input whenever possible. Quality of uncertainty
estimation is a subject of over or under confident prediction, which is often
not addressed in many models. In this paper we show several extensions to the
Gaussian Conditional Random Fields model, which aim to provide higher quality
uncertainty estimation. These extensions are applied to the temporal disease
graph built from the State Inpatient Database (SID) of California, acquired
from the HCUP. Our experiments demonstrate benefits of using graph information
in modeling temporal disease properties as well as improvements in uncertainty
estimation provided by given extensions of the Gaussian Conditional Random
Fields method.
