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Improving confidence while predicting trends in temporal disease networks
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.
Deep Attention Model for Triage of Emergency Department Patients
Optimization of patient throughput and wait time in emergency departments (ED) is an important task for hospital systems.
Modeling Customer Engagement from Partial Observations
It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period.
Semi-supervised learning for structured regression on partially observed attributed graphs
We also show that the method can be useful for optimizing the costs of data collection in climate applications via active reduction of the number of weather stations to consider.
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