2015
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
NeurIPS 2015poster
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models…