Improving confidence while predicting trends in temporal disease networks
This work addresses the need for reliable uncertainty estimation in healthcare decision-making, though it appears incremental as it builds on existing methods.
The paper tackled the problem of improving uncertainty estimation in temporal disease network predictions, showing that extensions to Gaussian Conditional Random Fields enhance uncertainty quality when applied to a California inpatient database.
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.