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For highly sensitive real-world predictive analytic applications such ashealthcare and medicine, having good prediction accuracy alone is often notenough. These kinds of applications require a decision making process whichuses uncertainty estimation as input whenever possible. Quality of uncertaintyestimation is a subject of over or under confident prediction, which is oftennot addressed in many models. In this paper we show several extensions to theGaussian Conditional Random Fields model, which aim to provide higher qualityuncertainty estimation. These extensions are applied to the temporal diseasegraph built from the State Inpatient Database (SID) of California, acquiredfrom the HCUP. Our experiments demonstrate benefits of using graph informationin modeling temporal disease properties as well as improvements in uncertaintyestimation provided by given extensions of the Gaussian Conditional RandomFields method.

Improving confidence while predicting trends in temporal disease networks