Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

A Continuous-Time HMM (CT-HMM) is an HMM in which both the transitions between hidden states and the arrival of observations can occur at arbitrary (continuous) times.

We present the first comprehensive framework for efficient EM-based parameter learning in CTHMM,

In this paper, we present novel EM algorithms for CT-HMM learning which leverage recent approaches for evaluating the end-state conditioned expectations in CTMC models.