Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data

@article{Fan2015HierarchicalGH,
  title={Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data},
  author={Kai Fan and Marisa C. Eisenberg and Alison Walsh and A. Aiello and K. Heller},
  journal={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2015}
}
  • Kai Fan, Marisa C. Eisenberg, +2 authors K. Heller
  • Published 2015
  • Computer Science
  • Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
The purpose of this study is to leverage modern technology (mobile or web apps) to enrich epidemiology data and infer the transmission of disease. We develop hierarchical Graph-Coupled Hidden Markov Models (hGCHMMs) to simultaneously track the spread of infection in a small cell phone community and capture person-specific infection parameters by leveraging a link prior that incorporates additional covariates. In this paper we investigate two link functions, the beta-exponential link and sigmoid… Expand
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References

SHOWING 1-2 OF 2 REFERENCES
Sensing the "Health State" of a Community
Convergence of a stochastic approximation version of the EM algorithm