Background: The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern.
Burkom Howard
Description
Real-time emergency department (ED) data from the BioSense surveillance program for ILI visits and ILI admissions provide valuable insight into disease severity that bridges gaps in traditional influenza surveillance systems that monitor ILI in outpatient settings and laboratory-confirmed hospitalization, but do not quantify the relationship between ILI visits and hospital admissions.
Objective
The purpose of this analysis is to gain understanding of the burden of influenza in recent years through analysis of clinically rich hospital data. Patterns of visits and severity measures such as the ratio of admissions related to influenzalike illness (ILI) by age group from 2007 to 2010 are described.
Referenced File
Description
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).
Description
Despite considerable effort since the turn of the century to develop Natural Language Processing (NLP) methods and tools for detecting negated terms in chief complaints, few standardised methods have emerged. Those methods that have emerged (e.g. the NegEx algorithm) are confined to local implementations with customised solutions. Important reasons for this lack of progress include (a) limited shareable datasets for developing and testing methods (b) jurisdictional data silos, and (c) the gap between resource-constrained public health practitioners and technical solution developers, typically university researchers and industry developers. To address these three problems ISDS, funded by a grant from the Defense Threat Reduction Agency, organized a consultancy meeting at the University of Utah designed to bring together (a) representatives from public health departments, (b) university researchers focused on the development of computational methods for public health surveillance, (c) members of public health oriented non-governmental organisations, and (d) industry representatives, with the goal of developing a roadmap for the development of validated, standardised and portable resources (methods and data sets) for negation detection in clinical text used for public health surveillance.
Objective: This abstract describes an ISDS initiative to bring together public health practitioners and analytics solution developers from both academia and industry to define a roadmap for the development of algorithms, tools, and datasets to improve the capabilities of current text processing algorithms to identify negated terms (i.e. negation detection).
Description
Unlike other health threats of recent concern for which widespread mortality was hypothetical, the high fatality burden of opioid overdose crisis is present, steadily growing, and affecting young and old, rural and urban, military and civilian subpopulations. While the background of many public health monitors is mainly infectious disease surveillance, these epidemiologists seek to collaborate with behavioral health and injury prevention programs and with law enforcement and emergency medical services to combat the opioid crisis. Recent efforts have produced key terms and phrases in available data sources and numerous user-friendly dashboards allowing inspection of hundreds of plots. The current effort seeks to distill and present combined fusion alerts of greatest concern from numerous stratified data outputs. Near-term plans are to implement best-performing fusion methods as an ESSENCE module for the benefit of OHA staff and other user groups.
Objective: In a partnership between the Public Health Division of the Oregon Health Authority (OHA) and the Johns Hopkins Applied Physics Laboratory (APL), our objective was develop an analytic fusion tool using streaming data and report-based evidence to improve the targeting and timing of evidence-based interventions in the ongoing opioid overdose epidemic. The tool is intended to enable practical situational awareness in the ESSENCE biosurveillance system to target response programs at the county and state levels. Threats to be monitored include emerging events and gradual trends of overdoses in three categories: all prescription and illicit opioids, heroin, and especially high-mortality synthetic drugs such as fentanyl and its analogues. Traditional sources included emergency department (ED) visits and emergency management services (EMS) call records. Novel sources included poison center calls, death records, and report-based information such as bad batch warnings on social media. Using available data and requirements analyses thus far, we applied and compared Bayesian networks, decision trees, and other machine learning approaches to derive robust tools to reveal emerging overdose threats and identify at-risk subpopulations.
Description
The Distribute project began in 2006 as a distributed, syndromic surveillance demonstration project that networked state and local health departments to share aggregate emergency department-based influenza-like illness (ILI) syndrome data. Preliminary work found that local systems often applied syndrome definitions specific to their regions; these definitions were sometimes trusted and understood better than standardized ones because they allowed for regional variations in idiom and coding and were tailored by departments for their own surveillance needs. Originally, sites were asked to send whatever syndrome definition they had found most useful for monitoring ILI. Places using multiple definitions were asked to send their broader, higher count syndrome. In 2008, sites were asked to send both a broad syndrome, and a narrow syndrome specific to ILI.
Objective
To describe the initial phase of the ISDS Distribute project ILI syndrome standardization pilot.
Referenced File
Description
Recently published studies evaluate statistical alerting methods for disease surveillance based on detection of modeled signals in a data background of either authentic historical data or randomized samples. Differences in regional and jurisdictional data, collection and filtering methods, investigation resources, monitoring objectives, and systemrequirements have hindered acceptance of standard monitoring methodology. The signature of a disease outbreak and the baseline data behavior depend on various factors, including population coverage, quality and timeliness of data, symptomatology, and the careseeking behavior of the monitored population. For this reason, statistical process control methods based on standard data distributions or stylized signals may not alert as desired. Practical algorithm evaluation and adjustment may be possible by judging algorithmperformance according to the preferences of experienced human monitors.
Objective
This presentation gives a method of monitoring surveillance time series on the basis of the human expert preference. The method does not require detailed history for the current series, modeling expertize, or a well-defined data signal. It is designed for application to many data types and without need for a sophisticated environment or historical data analysis.
Referenced File
Description
One objective of public health surveillance is detecting disease outbreaks by looking for changes in the disease occurrence, so that control measures can be implemented and the spread of disease minimized. For this purpose, the Florida Department of Health (FDOH) employs the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE). The current problem was spawned by a laborintensive process at the FDOH: authentic outbreaks were detected by epidemiologists inspecting ESSENCE time series and derived event lists. The corresponding records indicated that patients arrived at an ED within a short interval, often less than 30minutes. The time-of-arrival (TOA) task was to develop and automate a capability to detect events with clustered patient arrival times at the hospital level for a list of subsyndrome categories of concern to the monitoring counties.
Objective
This presentation discusses the approach and results of collaboration to enable a solution of a hospital TOA monitoring problemin syndromic surveillance applied to public health data at the hospital level for county monitoring.
Referenced File
Description
Recent years' informatics advances have increased availability of various sources of health-monitoring information to agencies responsible for disease surveillance. These sources differ in clinical relevance and reliability, and range from streaming statistical indicator evidence to outbreak reports. Information-gathering advances have outpaced the capability to combine the disparate evidence for routine decision support. In view of the need for analytical tools to manage an increasingly complex data environment, a fusion module based on Bayesian networks (BN) was developed in 2011 for the Dept. of Defense (DoD) Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). In 2012 this module was expanded with syndromic queries, data-sensitive algorithm selection, and hierarchical fusion network training [1]. Subsequent efforts have produced a full fusion-enabled version of ESSENCE for beta testing, further upgrades, and a software specification for live DoD integration. Beta test reviewers cited the reduced alert burden and the detailed evidence underlying each alert. However, only 39 reported historical events were available for training and calibration of 3 networks designed for fusion of influenza-like-illness, gastrointestinal, and fever syndrome categories. The current presentation describes advances to formalize the network training, calibrate the component alerting algorithms and decision nodes together for each BN, and implement a validation strategy aimed at both the ESSENCE public health user and machine learning communities.
Objective
This presentation aims to reduce the gap between multivariate analytic surveillance tools and public health acceptance and utility. We developed procedures to verify, calibrate, and validate an evidence fusion capability based on a combination of clinical and syndromic indicators and limited knowledge of historical outbreak events.
Description
The Armed Forces Health Surveillance Center (AFHSC) supports the development of new analytical tools to improve alerting in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) disease-monitoring application used by the Department of Defense (DoD). Developers at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have added an analytic capability to alert the user when corroborating evidence exists across syndromic and clinical data streams including laboratory tests and filled prescription records. In addition, AFHSC epidemiologists have guided the addition of data streams related to case severity for monitoring of events expected to require expanded medical resources. Evaluation of the multi-level fusion capability for both accuracy and utility is a challenge that requires feedback from the user community before implementation and deployment so that changes to the design can be made to save both time and money. The current effort describes the design and results of a large evaluation exercise.
Objective
To evaluate, prior to launch, a surveillance system upgrade allowing analytical combination of weighted clinical and syndromic evidence with multiple severity indicators.