This paper is in the following e-collection/theme issue:

Public Health Surveillance Practice (162) Theme Issue: Bayesian Methods In Medical Research (26) Presentations, Abstracts, or Demonstrations (740)

Published on in Vol 5, No 1 (2013):

Refinement of a Population-Based Bayesian Network for Fusion of Health Surveillance Data

Refinement of a Population-Based Bayesian Network for Fusion of Health Surveillance Data

Refinement of a Population-Based Bayesian Network for Fusion of Health Surveillance Data

Authors of this article:

Howard Burkom1 ;   Yevgeniy Elbert1 ;   Liane Ramac-Thomas1 ;   Christopher Cuellar1 ;   Vivian Hung1
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    The project was to refine a prototype population-based Bayes Network module for live implementation in the U.S. Department of Defense ESSENCE system to combine syndromic and clinical evidence sources to monitor health at hundreds of military care facilities. Evidence types included outpatient data records, laboratory tests, and filled prescription records. The multi-level approach included expanded data queries, data-sensitive algorithm selection, improved transformation of algorithm outputs to alert states, and hierarchical Bayesian Network training. Algorithmic and network thresholds were adjusted with stochastic optimization using 24 documented outbreak datasets.