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 Allison E. Aiello and Katherine A. Heller},
  journal={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2015}
}
  • Kai Fan, Marisa C. Eisenberg, K. Heller
  • Published 10 August 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… 

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References

SHOWING 1-10 OF 22 REFERENCES
Nested Hierarchical Dirichlet Processes
TLDR
A stochastic variational inference algorithm is derived for the model, which enables efficient inference for massive collections of text documents and alleviates the rigid, single-path formulation of the nCRP.
ISIS: a networked-epidemiology based pervasive web app for infectious disease pandemic planning and response
TLDR
Three recent studies illustrating the use of ISIS in real-world settings are described: uses of ISIS during the H1N1 pandemic, supporting a US military planning exercise, and distribution of limited stockpile of pharmaceuticals using public and private outlets.
A high-resolution human contact network for infectious disease transmission
TLDR
High-resolution data of CPIs during a typical day at an American high school is obtained, permitting the reconstruction of the social network relevant for infectious disease transmission and suggested that contact network data are required to design strategies that are significantly more effective than random immunization.
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
TLDR
An application to information retrieval in which documents are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP leads to clustering of documents according to sharing of topics at multiple levels of abstraction.
Hierarchical Dirichlet Processes
TLDR
This work considers problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups, and considers a hierarchical model, specifically one in which the base measure for the childDirichlet processes is itself distributed according to a Dirichlet process.
A Field-Validated Architecture for the Collection of Health-Relevant Behavioural Data
TLDR
A highly flexible, reconfigurable, and verifiable software architecture for monitoring health-related behaviours constructed using modern software engineering principles is presented, which includes targets as diverse as studying flu transmission and gamified interventions for sedentary behaviour.
Hidden Topic Markov Models
TLDR
This paper proposes modeling the topics of words in the document as a Markov chain, and shows that incorporating this dependency allows us to learn better topics and to disambiguate words that can belong to different topics.
A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms
TLDR
Two modifications to the MCEM algorithm (the poor man's data augmentation algorithms), which allow for the calculation of the entire posterior, are presented and serve as diagnostics for the validity of the posterior distribution.
Coupling a stochastic approximation version of EM with an MCMC procedure
The stochastic approximation version of EM (SAEM) proposed by Delyon et al. (1999) is a powerful alternative to EM when the E-step is intractable. Convergence of SAEM toward a maximum of the observed
Coupled hidden Markov models for complex action recognition
  • M. Brand, N. Oliver, A. Pentland
  • Computer Science
    Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1997
We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying
...
...