The motivation for this project is to provide greater situational awareness to DoD epidemiologists monitoring the health of military personnel and their dependents. An increasing number of data sources of varying clinical specificity and timeliness are available to the staff. The challenge is to integrate all the information for a coherent, up-to-date view of population health. Developers at the Johns Hopkins Applied Physics Laboratory, in collaboration with medical epidemiologists at the Armed Forces Health Surveillance Branch, previously designed a multivariate decision support tool to add to the DoD implementation of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE). Data sources included clinical encounter records including free-text chief complaints, filled prescription records, and laboratory test orders and results. Filtered data streams were derived from these sources for daily monitoring, and alerting algorithms were customized and applied to the resulting time series. We built BNs to derive overall levels of concern from the collection of data streams and algorithm outputs to derive, in the form of daily fusion alerts, the overall level of various outbreak concerns. Visualizations made apparent which data features accounted for these concerns, including drill-down to the level of patient record details. Advantages of the BN approach are this transparency and the capacity for assessments using incomplete data and incorporating novel and report-based data streams. The need for such fusion was nearly unanimous in a global survey of public health epidemiologists [1]. Our proof-of-concept system based on commercial BN software was well received by a cross-section of DoD health monitors. The new software tools we apply in this project use freely available R packages which provide more comprehensive tools for BN training and development. These results will allow us to improve the analytic fusion abilities of DoD ESSENCE, as well as in civilian surveillance systems Our testing procedures and results are presented below.
Objective: Our project goal is to enhance the capability of automating health surveillance[MOU1] by US Department of Defense (DoD) epidemiologists. We employ software tools that build and train Bayesian networks (BNs) to facilitate the development of analytic fusion of multiple, disparate data sources comprising both syndromic and diagnostic data streams for rapid estimation of overall levels of concern for potential disease outbreaks. Working with previously developed heuristic BNs, we evaluate the ability of machine learning algorithms to detect outbreaks with greater accuracy. We use historical training data on the ability to detect outbreaks of influenza-like illness (ILI).