We have outlined a probabilistic modeling system that is capable of predicting heart failure alarms using time series of vital signs. It is able to learn the key parameters from data (state and temporal resolution) and allows fast adaptation to personalized features of a specific patient. Tests involving a limited set of vital signs indicate improved predictability of heart failure events when compared to a model relying only on prior probabilities. The next steps involve adding more vital signs to the input space to realize improvements of predictive accuracy. 