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Mobile Health pp 361-387 | Cite as

Learning Continuous-Time Hidden Markov Models for Event Data

  • Yu-Ying Liu
  • Alexander Moreno
  • Shuang Li
  • Fuxin Li
  • Le Song
  • James M. Rehg
Chapter

Abstract

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive modeling tool for mHealth data that takes the form of events occurring at irregularly-distributed continuous time points. However, the lack of an efficient parameter learning algorithm for CT-HMM has prevented its widespread use, necessitating the use of very small models or unrealistic constraints on the state transitions. In this paper, we describe recent advances in the development of efficient EM-based learning methods for CT-HMM models. We first review the structure of the learning problem, demonstrating that it consists of two challenges: (1) the estimation of posterior state probabilities and (2) the computation of end-state conditioned expectations. The first challenge can be addressed by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by exploiting computational methods traditionally used for continuous-time Markov chains and adapting them to the CT-HMM domain. We describe three computational approaches and analyze the tradeoffs between them. We evaluate the resulting parameter learning methods in simulation and demonstrate the use of models with more than 100 states to analyze disease progression using glaucoma and Alzheimer’s Disease datasets.

Notes

Acknowledgements

Portions of this work were supported in part by NIH R01 EY13178-15 and by grant U54EB020404 awarded by the National Institute of Biomedical Imaging and Bioengineering through funds provided by the Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov). The research was also supported in part by NSF/NIH BIGDATA 1R01GM108341, ONR N00014-15-1-2340, NSF IIS-1218749, NSF CAREER IIS-1350983, and funding from the Georgia Tech Executive Vice President of Research Office and the Center for Computational Health.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yu-Ying Liu
    • 1
  • Alexander Moreno
    • 1
  • Shuang Li
    • 1
  • Fuxin Li
    • 2
  • Le Song
    • 1
  • James M. Rehg
    • 1
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.2077 Kelley Engineering CenterOregon State UniversityCorvallisUSA

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