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Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
Yu-Ying Liu · Shuang Li · Fuxin Li · Le Song · James Rehg

Tue Dec 08 04:00 PM -- 08:59 PM (PST) @ 210 C #27

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 or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer's disease dataset.

Author Information

Yu-Ying Liu (Georgia Tech)
Shuang Li (Georgia Tech)
Fuxin Li (Georgia Tech)
Le Song (Georgia Institute of Technology)
James Rehg (Georgia Tech)

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