2015 HierarchicalGraphCoupledHMMsfor

• (Fan et al., 2015) ⇒ Kai Fan, Marisa Eisenberg, Alison Walsh, Allison Aiello, and Katherine Heller. (2015). “Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783326

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Hierarchical+Graph-Coupled+HMMs+for+Heterogeneous+Personalized+Health+Data
• http://dl.acm.org/citation.cfm?id=2783258.2783326&preflayout=flat#citedby

Quotes

Author Keywords

• Burn-in gibbs em; dynamic bayesian modeling; heterogenous infection; learning; medicine and science; social networks

Abstract

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 link, both of which allow the development of a principled Bayesian hierarchical framework for disease transmission. The results of our model allow us to predict the probability of infection for each persons on each day, and also to infer personal physical vulnerability and the relevant association with covariates. We demonstrate our approach theoretically and experimentally on both simulation data and real epidemiological records.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 HierarchicalGraphCoupledHMMsfor	Katherine Heller Kai Fan Marisa Eisenberg Alison Walsh Allison Aiello			Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data				10.1145/2783258.2783326		2015